Post on 08-Dec-2021
Degree Project
Level: master’s degree
Structural and Functional Analysis of Lexical Bundles in Music Research Articles A Corpus-Based Approach
Author: Elena Novella Savelyeva
Supervisor: Annelie Ädel
Examiner: Jonathan White
Subject/main field of study: Applied English Linguistics
Course code: EN3077
Credits: 15 credits
Date of examination: 2021-06-02
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Abstract
Applied linguistics has lately been seen in studies of formulaicity of language operating
through recurrent word combinations. The present study deals with one type of word
combinations, namely lexical bundles (LBs), which are defined as a sequence of three or more
words that frequently co-occur in a particular register (Biber et al., 1999). The present study is
a corpus-based analysis of four-word lexical bundles extracted from Music research articles
(RAs). The Corpus of Music Research Articles (CMRA) of one million words was created in
order to perform structural classification of the retrieved lexical bundles and an analysis of
their functions. The CMRA includes 110 articles collected from international music journals
from various music subdisciplines. In order to find which lexical bundles were characteristic
of music research specifically, the findings were compared to previous research based on
other academic disciplines. The list of 218 lexical bundles was compared to the one of three
different subject areas (Jalilifar et al., 2016) with the purpose of identification of discipline-
specific LBs (n=102) which included 20 topic-specific bundles; and general lexical bundles
(n=116) which included 56 core bundles shared among Music and three subject areas (Art and
Humanities, Sciences and Social sciences). Structurally and functionally, the analysis of the
extracted lexical bundles demonstrated that native English expert writers predominantly used
preposition-based phrases (50%), with respect to structure; and research-oriented bundles
(74%), with respect to function. The findings have pedagogical applications and could be used
in courses in English for Specific Purposes. Keywords: formulaic language, lexical bundles, research articles, music, musicology, Corpus
linguistics
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Table of Contents
1 Introduction ............................................................................................................................. 5
2 Theoretical Background ........................................................................................................ 10
2.1 Formulaicity in Language .......................................................................................... 10
2.2 Lexical Bundles ......................................................................................................... 15
2.1.1 Structures and functions of lexical bundles ............................................................. 17
2.1.2 Lexical bundles across academic disciplines or subject areas ................................ 19
3 Methodology and Material .................................................................................................... 27
3.1 Journal Selection ....................................................................................................... 28
3.2 Corpus Creation ........................................................................................................ 31
3.3 Material Identification of Lexical Bundles ................................................................ 35
3.3.1 Identification of lexical bundles (RQ1) ................................................................... 37
3.3.1.1 Analytical steps, exclusion criteria and grouping ............................................ 38
3.3.2 Determining the coverage of general bundles over the discipline-specific in the
domain of music (RQ2) .................................................................................................... 42
3.3.3 Structural forms and functions of the lexical bundles (RQ 3 and 4) ....................... 44
4 Results ................................................................................................................................... 47
4.1 Lexical Bundle Identification .................................................................................... 47
4.2 Structural Forms ........................................................................................................ 48
4.3 Functional Types ....................................................................................................... 53
4.3.1 Research-oriented bundles ...................................................................................... 55
4.3.2 Text-oriented bundles .............................................................................................. 59
4.3.3. Participant-oriented bundles ................................................................................... 63
5 Conclusions ........................................................................................................................... 65
References ................................................................................................................................ 69
Appendix 1. The Full List of Lexical Bundles in the CMRA .................................................. 74
Appendix 2. The List of Overlaps in the CMRA ..................................................................... 81
Appendix 3. The list of the RAs used for the CMRA .............................................................. 83
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List of Tables
Table 1. Formulaic sequences of different levels of invariability ............................................ 11 Table 2. The structural and functional distribution of lexical bundles across disciplines ....... 23 Table 3. The distribution of journals and articles selected for the corpus................................ 29
Table 4. An example of the initial letter variation within the retrieved sequences .................. 40 Table 5. Two-words contracted combinations identified in the corpus ................................... 40 Table 6. A random sample of the matches count ..................................................................... 43 Table 7. Functional classification of lexical bundles by Salazar (2014) .................................. 44 Table 8. The structural forms of the lexical bundles in the Music research articles ................ 48
Table 9. Structural category “complete noun phrase” .............................................................. 50
Table 10. Noun-based category occurrences and frequencies compared ................................. 50 Table 11. Preposition-based category occurrences and frequencies compared ....................... 51
Table 12. Verb-based category occurrences and frequencies compared.................................. 52 Table 13. The functions of the lexical bundles in the Music research articles......................... 53 Table 14. The structural distribution of descriptive signals ..................................................... 56
Table 15. The quantification function of music LBs ............................................................... 59 Table 16. The participle-oriented bundles of music LBs ......................................................... 63
List of Figures
Figure 1. SketchEngine results of the note writing .................................................................. 33 Figure 2. Sketch Engine concordance lines of the note writing ............................................... 33
Figure 3. The initial text in the article by Raz (2014) .............................................................. 33
Figure 4. Sketch Engine results of graphs decodification ........................................................ 34 Figure 5. Sketch Engine concordance lines of the graphs decodification ................................ 34 Figure 6. The initial text of the article by Hooper (2019) ........................................................ 34 Figure 7. Frequency normalisation formula ............................................................................. 37
Figure 8. The demonstration of acknowledgement functions in the corpus ............................ 64
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1 Introduction
The motivations for this study originate in part from the author‟s own experience in teaching
English at a Musical higher educational institution (Rostov State Rakhmaninov Conservatoire
–RSC–, Russia). My personal and professional interest in the way the native English-speaking
musicologists use the language, especially its phraseological component, resulted in
investigations of onomatopoeia in music terminology (such as growl denoting a specific
sound of brass instruments or shuffle denoting a piano-playing technique, for instance) and
phraseological units in everyday language containing music vocabulary (such as, the tune the
old cow died of, or at sb’s whistle). These works were published in 2009 and 2013 in the
journal South-Russian Musical Anthology affiliated with the RSC. While performing the
above mentioned research, it became clear that the discipline of Music and Musicology had
not been investigated much before (or even not at all) from the perspective of language use.
Music, along with Theatre and Dance, is included into the Performing Arts subgroup; which,
in its turn, forms part of the Arts cycle, a subcategory of the Humanities subject area
(Britannica, 2021). The Humanities conventionally include the study of all languages and
literatures, the Arts, History and Philosophy. The study of music involves a host of different
areas (e.g., jazz studies, orchestra studies, ethnomusicology, musicology and music theory,
among others), many of which might be significantly different from any other while also
potentially informing each other. Musicology, for its part, is currently understood as “an all-
embracing term for the study of music that respects the whole musical field” (Beard & Gloag,
2016, p. xii). Colloquially, Humanities are called Soft sciences and are opposed to Hard,
technical, sciences.
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Both Soft and Hard sciences have been studied from the perspective of formulaicity. To date,
it has been shown that the teaching of language formulaicity has a lot of important use in the
ESP classroom and that “phraseological competence is an important part of nativelike, fluent,
and idiomatic language use” (Paquot & Granger, 2012, p. 130). It has also become evident
that phraseology plays an important role in foreign language learning and teaching since
“phraseology binds words, grammar, semantics, and social usage” together (Ellis, 2008, p. 5).
The general academic division of Arts and Humanities as a whole has been investigated from
the perspective of formulaicity: lexical bundles were analysed in studies by Kwary et al.
(2017), Durrant (2017), Jalililafar et al. (2016), among others. These works have investigated
the syntactic structures and functions of lexical bundles of various lengths across multiple
subject areas and academic disciplines, and will be further presented in overviewed in Section
2. Also, previous studies have identified shared and discipline-specific lexis. However, the
Performing Arts in general and Music more specifically are still under-investigated areas. For
this reason, the choice of the present material was made in order to make the picture of the
subject area of Arts and Humanities more complete.
These two issues signaled that there were some key components that could be useful for both
non-native professional musicologists writing in English and teachers of English for Specific
Purposes. All this stimulated the idea of the present work: to investigate the use of formulaic
language by native English-speaking experts in Music research articles (RAs).
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The choice of the genre for this study was also prompted by the professional interest of the
author. The act of communication among scientists and scholars is realised in the written form
mainly by means of research articles. A research article is considered to be one of the main
tools of the academic discourse community for internal communication; it is used to
communicate information of common interest between members of research communities
more efficiently (Cortes de los Rios & Cruz Martinez, 2000, p. 40) and is “meant to serve the
goals of specific discourse communities” (Bhatia, 1997, p. 181). As stated by Dudley-Evans
(1994), a genre is a means of achieving a communicative goal that has evolved in response to
particular rhetorical needs, and that will change in response to those needs. It involves certain
syntactic structures generally used in this genre and employs particular lexico-grammatical
resources.
Every discourse community uses multiple ways of communication, a combination of which
helps to develop various genres (Swales, 1990). A discourse community might be identified as
a group of people who share a set of discourses and interests between its members (Swales,
1988). It has its mechanisms for members‟ communication. In short, a discourse community
uses a genre, among other aspects, as a tool for the successful communication of its members.
The most representative features of the research article as a genre have been said to be the
specific generic structure, scientific vindication, communicative politeness, and indirect
language. These traits are described in more detail in the work by Cortes de los Rios and Cruz
Martinez (2000). Indeed, writing research articles appears to be a complex activity with many
visible and invisible layers, and preparing to write a research article involves an
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understanding of discourse functioning at a high level (Abdi et al., 2010). In other words, the
genre of the research article is a specific tool with its own complex procedures and features
that is used by the academic discourse community for its communicative interests.
With these personal and professional motivations in mind, this work is an attempt to examine
the lexical bundles used by native English-speakers in Music research articles, from the
perspective of structural forms and functions. The study will address these issues through the
research questions (RQs) listed below:
1. What four-word lexical bundles are found in the corpus?
2. How do the findings compare to those of previous studies based on the other
disciplines (Jalilifar et al., 2016)? More specifically, what LBs are specific for the
discipline of music?
3. What structural forms do the LBs have?
4. What functions do the LBs serve?
In terms of methodology, the present study uses a corpus-based approach, since corpus
linguistic techniques are considered to be “an extremely powerful tool for the analysis of
natural language” (Schmitt, 2010, p. 89) for exploring the above mentioned phraseological
material. In order to answer the RQs, a corpus of one million words has been created and 218
four-word lexical bundles have been retrieved from the corpus to be analysed from the
perspective of syntactic structures (based on the classification of Biber et al., 1999) and
discourse functions (based on the classification of Salazar, 2014, which is a modified version
of Hyland, 2008b).
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This dissertation is organized in the following manner: Section 2 provides an overview of
previous studies relevant to the present work. Section 3 describes the methods and procedures
used in the research. Section 4 presents the results of these studies. Finally, Section 5,
Conclusions, will discuss the key findings, and will also propose some pedagogical
applications of the material. The full list of lexical bundles is provided in Appendix 1,
Appendix 2 provides a list of overlaps in lexical bundles and Appendix 3 contains the list of
the research articles used for the Corpus.
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2 Theoretical Background
This section will overview the previous research on the formulaicity of language and lexical
bundle analysis published by multiple scholars over recent years. It will reflect the concept of
formulaicity in language and its realisations. It will introduce the main concepts of this
linguistic area and will deal with lexical bundles in detail. It will present the results of works
on lexical bundles in academic writing from different perspectives.
2.1 Formulaicity in Language
Thirty years ago Sinclair (1991) discussed the idea of the open-choice principle and the idiom
principle in language. The open-choice principle implies the traditional idea that practically
each position in a phrase or a sentence offers a choice. In the case of the idiom principle, a
language user has available a large number of semi-preconstructed phrases that constitute
single choices, “once a register choice is made […] all the slot-by-slot choices are massively
reduced” (Sinclair, 1991, p. 110). Preconstructed multi-word combinations, on the contrary,
lead to the idiom principle in the language and are connected to formulaicity in language.
Nowadays, many researchers stress that “language is largely formulaic in nature, and that
phraseological competence is an important part of native-like, fluent, and idiomatic language
use” (Paquot & Granger, 2012, p. 130). Moreover, it has been demonstrated that the English
language is formulaic and highly patterned in nature, and in everyday language use, it seems
that most spoken and written language use is more formulaic than based on novelty (Jensen,
2017). In other words, formulaicity can be said to be a key feature of the English language.
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According to some previous investigations in this area, multiple terms have been used to
describe formulaicity in language. It has been noted (Elturki, 2015, p. 12) that the English
language can be characterised by different types of language formulas such as collocations
(make a decision), idioms (to be fed up with), proverbs (Don’t cry over spilled milk), phrasal
verbs (call something off), and speech formulas (Excuse me). Other examples of terms that
describe language formulaicity and that have been extensively used in the studies of the past
two decades include the following: lexical bundles (Biber et al., 1999), chunks (Hyland,
2008a), multi-word combinations and academic formulas (Simpson-Vlach & Ellis, 2010),
formulaic sequences (Wray, 2002; Ädel & Erman, 2012) and n-grams (Cermakova &
Chlumska, 2017; Durrant, 2017). All of these terms have been used in the literature to refer to
“continuous word sequences retrieved by taking a corpus-driven approach with specified
frequency and distribution criteria” (Chen & Baker, 2010, p. 30). In order to avoid such
terminological ambiguity, this work has adopted the definition used by Wray (2002),
according to which a formulaic sequences (FSs) are “continuous or discontinuous” and appear
to be “prefabricated and stored and retrieved whole from memory at the time of use, rather
than being subject to generation or analysis by the language grammar” (Wray, 2002, p. 9).
Furthermore, Biber et al. (1999, pp. 988-989) provide an extensive description of formulaic
sequences of different levels of invariability. Table 1 reflect the main concepts of language
formulaicity:
Table 1. Formulaic sequences of different levels of invariability
Idioms These are supposed to be relatively invariable with meaning that
cannot be predicted from the meaning of its parts, they have to be
learned as a whole.
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Lexico-grammatical
association (think +
that expression)
These are co-occurrence patterns with which the words can have
strong associations.
Collocations These are associations between lexical words that co-occur more
frequently than expected by chance; the individual words in a
collocation retain their own meaning. In case of collocations,
linguistics deals with two very different concepts (Palmerian and
Firthian) which “have caused considerable confusion”, according to
Lindquist and Levin (2018). The term by Palmer refers to collocation
as “a succession of two or more words that must be learnt as an
integral whole and not pieced together from its component part”
(Palmer, 1933, title page), among the examples of collocations are
“to have a hard time of it” or “to hear something [anything, nothing,
etc.] of [about] N3” (Lindquist & Levin, 2018, pp. 72–73). This type
is closer to linguistic structures and has a wider use in the linguistic
literature.
Otherwise, the Firthian way puts greater emphasis on how the
meaning of individual words is influenced by other words that a
given word frequently occurs together with. Following the Firthian
tradition, many scholars assume a statistical definition of collocation.
According to this view, collocations are statistically significant co-
occurrences of two or more words regardless of the meaning of these
word combinations. Moreover, another important term is collocates,
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which refers to words “which occur in the vicinity of the keyword but
which do not necessarily stand in a direct grammatical relationship
with it” (Lindquist & Levin, 2018, pp. 73). This type of collocation
has been called “window collocations” due to the span of the window
of varying size; commonly research tends to take into consideration
four or five words to the left and to the right of the node word. It
seems reasonable to conclude that this definition of collocation is
very close to lexical bundles.
n-grams These are sequences of continuous N words (e.g. 2-gram, 3-grams,
etc.), regardless of their frequency, and could both include less and
highly frequent co-occurrences. In some studies, however, the terms
lexical bundle and n-gram have been used to differentiate the level of
their frequencies in the corpus: when studying four-word formulaic
sequences, Durrant (2017) used the term 4-gram to refer to all four-
word combinations, regardless of frequency and the term lexical
bundle “would be reserved to refer to high-frequency 4-grams”
(Durrant, 2017, p. 170). This use of the term could also be found in
the work of other authors (Cermakova & Chlumska, 2017).
Lexical bundles These are recurring sequences of three or more words frequently
occurring in a collection of texts and most commonly co-occuring in
a register (Biber et al., 1999, p. 990). They are recurrent expressions
that, regardless of their idiomaticity and their structural status, go
together in natural discourse. Also, “they typically do not coincide
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with traditional grammatical units, but instead represent clause or
phrase fragments” (Ädel & Erman, 2012, p. 82). Lexical bundles tend
not to match up with traditional linguistic units and usually bridge
two or more phrases or clauses (typical examples include: if you look
at; that’s one of the; it’s important to; (Durrant, 2017, p. 166). They
are different from idiomatic expressions and assume important
discourse functions in the language (Biber & Barbieri, 2007).
Based on the above, it seems reasonable to formulate the properties of formulaic sequences as
follows: they can be identified automatically; they play definable functional roles; and they
are highly sensitive to differences between text types (Durrant, 2017, p. 166). The last
property turns to be very useful for the identification of variation across disciplines. In written
academic genres, in general, it has been shown that formulaic sequences display a higher
frequency than in non-academic genres (Biber et al., 1999; Hyland, 2008; Simpson-Vlach &
Ellis, 2010; Wood, 2015). The types of formulaic sequences depend on context and genre.
Hyland has emphasised that “[t]he extensive use of such pre-fabricated sequences as it has
been noted that in academic written genres helps (…) to signal the text register to readers and
reduce processing time by using familiar patterns to link elements of new information”
(Hyland, 2008b, p. 5).
Due to the fact that the discourse community possesses “an inbuilt dynamic towards an
increasingly shared and specialized terminology” (Swales, 1988, p. 212), there has been a key
debate concerning to what extent it could be true to say that there exists a common core
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vocabulary across a range of academic texts and disciplines (Flowerdew, 2015). As
demonstrated in some previous research, lexical bundles have been shown to be discipline-
bound (Cortes, 2004; Hyland, 2008b), with each discipline or academic community having its
own unique recurrent word-combinations. However, the alternative theory of common-core
lexical bundles across academic disciplines has argued that disciplines tend to share formulaic
formulas and this means that it is possible to identify core bundles across academic disciplines
or, even more, subject areas. This idea was formulated by Simpson-Vlach and Ellis (2010)
and has been supported by Paquot (2010) and Jalilifar et al. (2016) among others. The present
work will follow this theory of core bundles by looking at the bundles shared by different
academic disciplines listed in Jalilifar et al. (2016). At the same time, it will also identify
discipline specific, topic-specific and general lexical bundles in Music RAs. The purpose of
the present work is to look at the domain of music from the perspective of lexical bundle
analysis by means of a corpus of Music research articles.
2.2 Lexical Bundles
Previous research has studied LBs from different perspectives, focusing on how they are used
by different groups of users, such as learners at different proficiency levels compared to
native speakers, or how they are represented in academic writing across disciplines. Some of
the most important investigations and influential theories on corpus-based lexical bundle
analysis include various works on the most common variables.
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The extent to which LBs vary across registers or genres has been discussed in the works by
e.g. Biber and Barbieri (2007), Biber, (2009), Biber et al. (1999) and (2004), Biber (2006),
Hyland (2008a, 2008b), Simpson-Vlach and Ellis (2010) and others.
The extent to which LBs vary depending on the L1-L2 status of the speakers, e.g. native-
speaker perspective versus language-learner perspective, has been investigated, among others,
by Ädel and Erman (2012), Chen and Baker (2010), Du (2013), Esfandiari and Barbary
(2017), Karabacak and Qin (2013), Nekrasova (2009), Pan et al. (2016), Paquot and Granger
(2012), Paquot (2017), Perez-Llantada (2014), Römer (2009), Staples et al. (2013) and others.
Researchers have also compared the use of bundles by academic writers with different first
languages and different levels of writing expertise. Key studies in this category have been
carried out by Cortes (2004), Gil and Caro, (2019), Ozturk and Kose (2016), Pan and Liu
(2019), Qin (2014) and others.
The extent to which LBs vary across academic disciplines or subject areas has been addressed
in the works by Durrant ( 2017), Kwary et al. (2017), and Salazar et al. (2013), and others.
More specifically, the structural and/or functional features of LBs across disciplines have
been investigated by Esfandiari and Barbary (2017), Jalali et al. (2015), Jalilifar and
Ghoreishi (2018), Lee and Lee (2018), Pan et al. (2016), Qin, (2014), Verdaguer et al. (2013)
and others.
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Since the present work deals with the disciplinary features of Music RAs, their structures and
functions, it is relevant to pay special attention to works previously published in this area.
2.1.1 Structures and functions of lexical bundles
EAP research published in recent years has focused on determining the functional patterns
and syntactic structures of lexical bundles of different academic genres and disciplines.
Syntactic structures of lexical bundles have been classified by Biber and others (Biber et al.,
1999) based on three main categories: noun-based, preposition-based, and verb-based
bundles. Each category is then further subdivided into smaller subcategories.
From a functional perspective, Biber et al. (2004) distinguished between three main
categories: stance bundles, discourse organizers and referential bundles. Stance bundles are
supposed to express attitudes (it is possible to, it is necessary to) or assessments of certainty
(the fact that the). The stance expression shares epistemic stance and modality stance
functions. Discourse organizers serve to reflect relationships between prior and coming
discourse (as well as the). Within this category, there are topic introduction bundles (in this
chapter we) and topic elaboration bundles (on the other hand) that are used to organize and
structure texts. Referential bundles help make direct reference to physical or abstract entities
(one of the most), or to the textual context itself (in the form of). The category of referential
bundles can be also characterized by the function of attribute specification: under this
subcategory, there can be found place markers (in the United States), time markers (at the
same time), framing bundles (the extent to which), quantifying bundles (the rest of the), text
deixis (shown in figure N) and multi-functional references (at the end of).
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The classification by Hyland (2008b, p. 13) proposes a modified version of Biber et al.‟s
(2004) categories, with labels that are especially relevant to the domain of academic writing.
Hyland‟s classification included the following distribution (see Hyland, 2008b, pp. 13–14):
research-oriented (location (at the beginning of), procedure (the use of the),
quantification (a wide range of), description (the structure of the), topic (the currency
board system));
text-oriented (transition signals (on the other hand), resultative signals (as a result
of), structuring signals (in the present study), framing signals (with the respect to the));
participant-oriented (stance features (are likely to be), engagement features (it
should be noted that)).
The work by Qin (2014) developed the classification of Biber et al. (2004) and changed the
order of the functions: referential, text-organisers, stance and other bundles. The work also
reorganised the first category of referential bundles which would include place markers (in the
present study), time markers (at the same time), descriptive bundles (the aim of the) and
quantifying bundles (there are a number of). Four new sub-types were added to the category
of text-organisers: brevity (and so forth), exemplification (for example, the), explanation (that
is to say), and locator (as noted earlier). Under the category of stance bundles, there were two
specific sub-types: epistemic-impersonal/probable-possible (it is likely that) and other stance
bundles (it was found that the). The fourth category, the other bundles, referred to subject-
specific bundles (in a foreign language setting). However, these were not defined by the
author, so it was not quite clear what was included in this subcategory. The classification of
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Qin (2014) could be called a blend between the classification of Biber et al. (2004) and
Hyland (2008b) since the taxonomy is similar to both of these in one way or another.
The classification by Salazar (2014) is based on the one by Hyland (2008b), but introduced
some additional subcategories such as grouping (in the research-oriented category) and
acknowledgments (in the participant-oriented category). In fact, the extended functional
taxonomy of target bundles was presented by the same author, in collaboration with others
(Salazar et al., 2013, p. 45) but in a different work. In the elaborated version, the research-
oriented section included 8 subcategories, the text-organisers contained 20 subcategories and
the reader-oriented section consisted of 10 subcategories. This kind of extended taxonomy
was supposed to assist researchers to achieve a more precise functional identification in case
any classification issues occurred.
The present study employed the functional classification by Salazar (2014). The main reason
for opting for it is the intent to make this research more comparable with the results of
different studies Jalilifar et al. (2016) and Jalilifar and Ghoreishi (2018) who used this
classification.
2.1.2 Lexical bundles across academic disciplines or subject areas
As mentioned above, there exist a large amount of studies that focus on cross-disciplinary
lexical bundle analyses: Durrant (2017), Johnston (2017), Kwary et al. (2017), Qin (2014),
Salazar et al. (2013), Staples et al. (2013) can be named, among others. This section is an
overview of the work on lexical bundle analysis across academic disciplines. Due to the fact
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that the present study looks into the use of four-word lexical bundles, their structures and
functions, the results regarding four-word sequences seemed especially relevant and important
since the investigations described below also employed the same structural (Biber et al., 2004)
and functional (Hyland, 2008b) classification as the present study has adopted. This section
will provide the results obtained from research on Computer science, Psychology, Health
science and Telecommunications and also comment on other cross-disciplinary studies in
different subject areas.
Medicine is one of the disciplines that have been studied from the perspective of lexical
bundle analysis. The study by Jalali et al. (2015) performed the structural and functional
analysis of lexical bundles in medical research articles. A corpus of just under 2.5 million
words was extracted from 790 research articles across 33 medical disciplines. The identified
lexical bundles were classified structurally based on Biber et al.‟s (1999) structural taxonomy
and functionally based on Hyland‟s (2008b) functional taxonomy. The focus was on 102
different four-word lexical bundles occurring minimally 20 times per million words.
According to the results, the largest structural category of lexical bundles were prepositional
phrases (44.5%). The verb-based phrase group ranked second (26.87%). Noun phrases
amounted to roughly twenty per cent (20.42%) of the data. In terms of functional distribution,
37% of the bundles belonged to research-oriented group and were used to describe time,
place, size and magnitude, the study itself, and research procedures in academic texts. It was
also found that medical research articles were characterized by a heavy use of text-oriented
clusters (42.5%), especially framing signals. Participant-oriented bundles had the lowest
frequency (21%).
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The discipline of Telecommunications was studied by Pan et al. (2016) and explored the
writing of L1-Chinese experts writing in English compared with L1-English experts. The
research moderated the structural classification by Biber et al. (1999) and Hyland (2008b).
They mixed the labels of both classification in the way that Biber‟s „referential functions‟
group was referred to as „research-oriented‟ and Biber‟s „discourse-organisers‟ functions
became „text-oriented‟. However, the authors found Hyland‟s label of „participant-oriented‟
bundles problematic when applied to academic writing, and for that reason adopted instead
the label „stance-oriented‟ bundles for the third category. The results showed some structural
differences between LBs used by L2 and L1 writers: L1 writers employed mostly phrasal
bundles consisting of noun phrase and prepositional phrase fragments, while L2 writers used
mainly clausal bundles consisting of verb phrases and clause fragments. Another key finding
was that both groups used similar proportions of functional distributions of types and tokens,
in that namely the text-oriented types turned out to be the largest category in both the
Telecommunication English Corpus and the Telecommunication Chinese Corpus.
The discipline of Psychology research articles was discussed by Esfandiari and Barbari
(2017). The work contrastively examined the four-, five-, and six-word lexical bundles in a
corpus of over 4 million words. The corpus was divided into two sub-corpora: the English
corpus (EC) and the Persian corpus (PC). The study successively performed a structural
analysis of four-word bundles and their functions in EC and PC, then following onto the
structural and functional analysis of five-word bundles and consequently, to the structural and
functional analysis of six-word bundles. Since the present study focuses on disciplinary
22
lexical bundle analysis and not on the analysis of native/non-native lexical bundles use, this
section will comment on the results obtained from the English corpus only. Moreover, the
results on four-word bundles will be discussed here. Firstly, the results demonstrated that
English writers significantly used lexical bundles that incorporate verb phrase fragments
(42%), lexical bundles that incorporate noun phrases accounted for 27% and prepositional
phrase fragments yielded 28% (Esfandiari & Barbary, 2017, p. 35). Functionally, English
writers mostly used text-oriented four-word bundles (42.5%) which were followed by
research-oriented (31.5%) and participant-oriented (26%).
Lexical bundles in Computer science research articles were studied by Lee and Lee (2018).
For that particular investigation, a Computer Science Corpus (CSC) had been compiled,
which included roughly 1.3 million words. The focus was on four-word lexical bundles that
occurred minimally 20 times per million words. The results demonstrated that, with regard to
the structural types, noun phrases (NPs) and prepositional phrases (PPs) occurred more
frequently than verb phrases (VPs). The analysis also revealed that the most pronounced
functional aspect of lexical bundles were discourse organizers, namely the ones with topic
introduction functions (in this article we, in this section we). The LBs referring to size,
amount, number, or quantity (the size of the, the total number of, is the number of) dominated
among referential lexical bundles. Unfortunately, no data pertaining to the functional
distribution of the lexical bundles were provided in the article.
The table below presents a comparison of the above mentioned results of four-word lexical
bundles used by English native writers in RAs across academic disciplines. It should be noted
23
that not all the authors conveyed the information explicitly in their work and in such a case
the percentage had to be double-checked manually in order to be reflected in the table below.
The highest number in each category is highlighted in grey in order to make the comparison
more visible.
Table 2. The structural and functional distribution of lexical bundles across disciplines
Discipline Structural types Functions
NPs PPs VPs Others Research-
oriented/
Referential
Text-
oriented/
Discourse
organisers
Participant
oriented/
Stance
Computer science
(Lee & Lee, 2018)
72 % 28 % - - - -
Psychology
(Esfandiari & Barbary,
2017)
27 % 28 % 42 % 3 % 31.5 % 42.5 % 26 %
Telecommunication
(Pan et al., 2016)
36.4 % 32.6 % 25.5 % 5.5 % 43.3 % 48.5 % 8.2 %
Medicine
(Jalali et al., 2015)
20.42 % 44.5 % 26. 87% 8.18 % 36.5 % 42.5 % 21 %
As shown in the table, functionally, text-oriented bundles predominate in every discipline;
while structurally, the results are not that homogeneous, demonstrating the leading role of
VPs in Telecommunications, PPs in Medicine and VPs in Psychology. In the case of
Computer sciences, the exact proposition of VPs and PPs was unclear, though amounted to
72 % in total.
Another interesting observation was formulated in the work by Johnston (2017), who
compared the use of four-word LBs among professional writers and language learners in two
disciplines: Applied linguistics and Literature. The functional realisation of the professional
use of the bundles is worth noting: professional writers in Applied linguistics and Literature
had a similar distribution of functional bundles. Professionals in Literature used text-oriented
24
bundles with the greatest frequency, accounting for about half of all bundles used. In Applied
linguistics, professionals used both research- and text-oriented bundles with similar
frequencies, accounting for around 45% each of total functional bundles. Both groups used
participant-oriented bundles the least, less than 10% of all bundles in each group.
Some authors have also studied the structural distribution of LBs in a collection of RAs from
a range of different disciplines, i.e. lexical bundle analyses in multiple subject areas. Three
general subject areas of Science, Social sciences and Art and Humanities were analysed by
Jalilifar et al. (2016) and Jalilifar and Ghoreishi (2018). The Arts and Humanities cycle
consisted of Arts, Literature, Applied linguistics, Philosophy and Religion. Each sub-corpus
amounted to 2 million words. By aiming to identify the common three-, four-, and five-word
bundles across three subject areas, the work supported the common-core bundles theory
previously expressed by Simpson-Vlach and Ellis (2010). Furthermore, when the core bundles
were identified, the study analysed core bundles for their discourse functions (adopting
Salazar‟s 2014 taxonomy). Functional analysis revealed the predominance of text-oriented
core bundles (58.5%), followed by research-oriented bundles (32.5%); and finally,
participant-oriented bundles were the least frequently used (9%).
In a different study, four subject areas - Health sciences, Life sciences, Physical sciences, and
Social sciences - were investigated by Kwary, Ratri and Artha (Kwary et al., 2017). This
study found both similarities and differences in the use of the 62 LBs in RAs across four
subject areas. The results demonstrated that Physical sciences used the most frequent number
of lexical bundles (n=43), followed by Social sciences (n=27) and Life sciences (n=12), while
25
Health sciences used the least amount (n=3). It was also pointed out that no lexical bundles
were found to be used in all four disciplines, which implied that there were differences in the
use of lexical bundles across different disciplines; in other words, no core bundles were
identified as shared throughout the four subject areas, which appeared to be an important
point and a key issue of the study. Due to the above-mentioned finding, the further technique
of pairing up the disciplines took place. This move was applied in order to see which
disciplines shared the greatest number of lexical bundles. The results were as follows: pair-up
of Life sciences with Physical sciences yielded eight shared bundles, then Life sciences and
Social sciences demonstrated six shared bundles and finally, the pair-up of Physical sciences
and Social sciences resulted in fourteen shared bundles. This method demonstrated that
Physical sciences and Social sciences shared the largest number of LBs; however, there were
no LBs shared between Health sciences and Physical sciences, nor between Health sciences
and Social sciences. Finally, after having identified the use of 62 lexical bundles across the
four subject areas, the distribution of the structures (Biber et al., 1999) and functions (Biber et
al., 2003) of LBs across the different academic disciplines was performed. The results showed
that referential expressions (40 LBs) outnumbered discourse organizers (12 LBs) and stance
expressions (10 LBs). Further analysis showed that stance expressions, particularly epistemic
stance which functions to hedge claims, were more frequently found in Social sciences than in
the other academic disciplines.
Summing up, previous studies have demonstrated that in many disciplines, the most common
structural types are NPs and PPs types, while the mostly used functional types are text-
oriented or discourse organisers with the exception of Medical RAs where the predominant
26
type was research-oriented functions. Generally, it can be said that a corpus-based study tends
to provide an unusually strong comparability of the material since lexical bundles are
automatically extracted from the corpus. Furthermore, in order the comparison could be
performed, it is important to respect the main criteria as corpus size, structural classification
used and register should be respected. However, there is always the risk of subjective or
misleading functional classification of the lexical bundles since qualitative analysis is
performed manually.
27
3 Methodology and Material
Corpus linguistics has provided the linguistic community with a lot of statistically significant
results. Corpora are playing an increasingly important role both in linguistic research and
language teaching. This work has employed the following procedures: corpus creation,
retrieval of lexical bundles, identification of general and discipline specific LBs, and,
subsequently, the structural and functional analysis of the retrieved LBs
The present study used a mixed-method design as “mixed methods became the dominant
paradigm and are typically seen to provide researchers with the best of both words” of
qualitative and quantitative analyses (Angouri, 2018, in Litosseliti, 2018, p. 35).
The quantitative analysis used in this work employed statistical, mathematical and
computational techniques by counting the frequencies, overlaps and other results on formulaic
sequences that occurred in the corpus.
Likewise, the process of the structural and functional classification required qualitative
analysis. In order to obtain more accurate data, the concordance lines were consulted in
context in order to “establish exactly which one of the functional categories lexical bundles
belong to” (Jalilifar et al., 2016, p. 186). In other words, the qualitative analysis meant
checking concordance lines in order to see the phrase in context.
28
3.1 Journal Selection
The selection of material for the corpus focused on Music research articles written by native
expert writers from three English-speaking countries where English is a native language or
L1. In case of this study, publications from the UK, the USA and Australia were chosen.
There were two main criteria that determined the article selection. First of all, the source
should be on-line and accessible, meaning that the journal articles can be freely accessed and
downloaded. Secondly, the article should be written by a native English speaker originating
from an inner-circle English speaking country (Kachru, 1985, pp. 12ff). This research
followed the tradition of Pan et al. (2016) with the methods proposed by Wood (2001) that
“operationally defined „L1-English‟ writers to be any author affiliated with an institution in a
country where English is spoken as the first language who also has a first and last name that
can be considered native to English-speaking countries” (Pan et al., 2016, p. 63). In this case,
qualitative analysis was applied, namely that the authors were selected by their affiliation to
higher educational institution in the UK, the USA, or Australia. In case of doubt, the writers‟
origins and their names were double-checked in open on-line sources.
Finally, a list of 110 research articles was composed. It covered twelve years, with works
from 2008 to 2020. The number of articles retrieved from each journal varied between eight
and twenty and can be seen in the table below (Table 3). The English language variations
(BrE, AmE, AusE) were not significant for this study and were not taken into consideration
for the lexical bundle analysis.
29
Table 3. The distribution of journals and articles selected for the corpus
Country
(sub-
corpus)
Journal Number of
articles
Journal description
Subdiscipline
UK
Total:38
IASPM Journal 20 articles IASPM Journal is the peer-
reviewed open-access e-journal
of the International Association
for the Study of Popular Music,
IASPM. It aims to publish
research and analysis in the field
of popular music studies.
Popular music
Music
Performance
research (the
UK)
13 articles Music Performance Research is
an open access journal. Its aims
to spread theoretical and
empirical research on the
performance of music. Specific
topics have been dealt with
include the role of music
performance in personal
development, identity,
communication and interaction;
the training and health of skilled
musicians; theories and models
of music performance; and the
foundations of musical expertise.
Performance of
music
Dancecult
(Dancecult:
Journal of
Electronic
Dance Music
Culture)
5 articles Dancecult is a peer-reviewed,
open-access e-journal for the
study of electronic dance music
culture (EDMC). The journal
includes research exploring the
sites, technologies, sounds and
cultures of electronic music in
historical and contemporary
perspectives.
Electronic
dance music
The USA
Total:36
Current
Musicology
Columbia (the
USA)
13 articles Current Musicology is a leading
journal for scholarly research on
music. It contains articles and
book reviews in the fields of
historical musicology,
ethnomusicology, music theory,
and philosophy of music are
published here. The journal was
founded in 1965 by graduate
students at Columbia University
as a semi-annual review.
History and
theory of music
Gamut (the USA) 13 articles Gamut is the peer-reviewed Music theory
30
online journal of the Music
Theory Society of the Mid-
Atlantic. It focuses on any aspect
of music theory and its related
disciplines.
and other
related
disciplines
Journal of jazz
studies (the
USA)
10 articles The Journal of Jazz Studies
(JJS), formerly the print journal
Annual Review of Jazz Studies, is
an open-access online journal,
which is peer reviewed and
published by the Institute of Jazz
Studies at Rutgers, The State
University of New Jersey. It
deals with jazz studies on a
different level: from technical
analyses to oral history to
bibliography to cultural
interpretation.
Jazz
Australia
Total:36
Context: A
Journal of Music
Research
(Australia), the
university of
Melbourne
20 articles
Context is a peer-reviewed
international music journal that
publishes original research
concerning all aspects of music
and music-related fields. In
addition to articles, Context
presents reviews of recent
publications interviews with
composers and practitioners, and
reports on ongoing research
projects. It is produced within the
Melbourne Conservatorium
of Music at the University of
Melbourne.
All aspects of
music
Dancecult 8 articles Mentioned above
Journal of Music
Research online
8 articles Is a peer-reviewed journal
published by the Elder
Conservatorium, Faculty of Arts
at The University of Adelaide. It
publishes English language
scholarly research articles in
areas including composition,
early music, ethnomusicology,
gender studies in music,
interdisciplinary studies in music,
music education, music
technologies, musicology, music
theory and analysis, opera,
performance practice, popular
music, ludomusicology.
All aspects of
music
31
As can be seen from Table 3, music journals tend to be attached to a specific university and
institution. This is much less common in other fields. Linguistics, for example, has a range of
truly international journals tied to publishers rather than specific university affiliations. Also,
Music as an academic discipline seems to be more interdisciplinary “as the boundaries
between different types of music are partially erased” (Beard & Gloag, 2016, p. xv). For the
present study, the music specialisations (or subdisciplines) were not taken into consideration
when selecting journals. However, it could be an interesting area for future research.
As can be seen from the table, the journals from each country, UK, the USA and Australia,
were represented by 38, 36 and 36 articles, respectively, producing the total number of 110.
The complete list of articles used for the Corpus are included in Appendix 3.
3.2 Corpus Creation
The most common way of retrieving formulaic sequences has recently been the use of a
corpus-handling tool, that is, a program that presents all the instances of a linguistic item in
their immediate context. In this study, SketchEngine (SE) has been used as the main software.
It generally allows the creation of a new corpus by uploading different kinds of documents,
such as .txt, .doc and .pdf formats. It also serves to extract collocations in a range of
grammatical patterns and then to organize “retrieved collocates according to the grammatical
relation to the headword (lemmas)” (Bhalla & Klimcikova, 2019, p. 267). It has a classic
“KWIC” display where each occurrence is shown on a single line, with the search item in the
middle and the context on each side.
32
Unlike traditional concordance software like AntConc and WordSmith, SketchEngine accepts
non-modified PDF files and in such a way no preliminary refining or clearing from non-text
materials as page numbers, references, figures and tables is needed. This feature turned out to
be an advantage timewise.
In case of this study, the direct uploading of the proper articles in PDF format resulted in
some specific symbols which appeared in the concordance lines, i.e. the note writing and the
graphs were interpreted by the software as odd symbols or sequences.
As can be seen from the figures below, the note symbols have been interpreted by the
software in a special way and produced significant statistical results of more than 1,000
entries (see Figure 1, Figure 2, Figure 3). The same thing happened with the decodification of
the graphs: it resulted in capital letter sequences (see Figure 4, Figure 5, Figure 6)
33
Figure 1. SketchEngine results of the note writing
Figure 2. Sketch Engine concordance lines of the note writing
Figure 3. The initial text in the article by Raz (2014)
34
Figure 4. Sketch Engine results of graphs decodification
Figure 5. Sketch Engine concordance lines of the graphs decodification
Figure 6. The initial text of the article by Hooper (2019)
35
As it can be seen from the visuals above, the note sequences and the graphs were recognised
by the software in a special way which was reflected in the corpus concordance lines and gave
a number of interesting results. For nomenclature reasons, the above mentioned outliers (in
statistics, an outlier is a data point that differs significantly from other observations) and
sequences would be referred to as noise sequences throughout this work and will be treated in
a special way in the present work they are not linguistic/verbal, so do not represent co-
occurring words and might cause significant issues in a statistical analysis. Patterns involving
the use of notes in Music RAs may still be of interest, but not for this specific study.
Summing up, in spite of some issued with the decodification, the SketchEngine tool turned out
to be very useful for the data collection and it made it possible to create the Corpus of Music
Research Articles (CMRA), amounting to almost one million words (n=998,468)
3.3 Material Identification of Lexical Bundles
This study focuses on four-word sequences, with a raw frequency of more than 10
occurrences per million words and occurring in a minimum of three texts. The analysis
consists of three parts. The first concerns frequency, the second focuses on the structural type,
and the third concerns function.
The choice of the number of words in the N-grams was motivated by tradition. Firstly, the
present study followed the practice of previous research (Ädel & Erman 2012; Kwary et al.,
2017; Pan et al., 2016), which has dealt with four-word sequences. Secondly, this work
focused on 4-grams as they seemed to be more suitable “for manual categorization and
36
concordance checks” (Chen & Baker, 2010, p. 32), and “they are far more common than 5-
word strings and offer a clearer range of structures and functions than 3-word bundles”
(Hyland, 2008b, p. 8). Thirdly, despite the fact that the study of Simpson-Vlach and Ellis
(2010) demonstrated the importance of three-word sequences, it appeared reasonable not to
focus on 3-grams due to the following reason: after a brief look at the three-word strings in
the corpus, it turned out that this cut-off criterion resulted in a lot of overlaps. Also, as
demonstrated by Ädel and Erman (2012), many three-word bundles are often included in
four-word bundles. Consequently, it seemed reasonable to postpone the analysis of three- and
five-word sequences also given the limited scope of the present study.
Bundles also need to occur across a range of texts. This range is necessary because if a lexical
bundle occurs across many texts, it is more likely to be a formulaic sequence than simply an
idiosyncrasy of the author (Biber, 2009). The frequency cut-off criterion of the present work
was also aligned with previous research and was set to a minimum of 10 times per million
words: Biber et al. (1999) identified that the most common lexical bundles occurred at least
10 times per million words. For the present study, the dispersion criterion focused on the
bundles that occurred over a range of three texts since the corpus is relatively small in size.
With respect to the corpus size, which is very close to a million of words, it was not deemed
necessary to apply normalization. However, normalisation will be considered due to the
further comparison of the results of this study with those of Jalilifar et al. (2016). In such a
way, normalization “is a way to adjust raw frequency counts from texts of different lengths so
that they can be compared accurately” (Biber et al., 1998, p. 263). Normalized frequency is
37
calculated by dividing the number of occurrences of formulaic sequences by the total number
of words in a particular corpus (Elturki, 2015, p. 37). In any case, if normalization had been
applied, it would not have affected the results significantly. For instance, the frequency per
corpus would be counted as follows:
Figure 7. Frequency normalisation formula
(10 / 998,468) x 1,000,000 = 10.01 words per corpus
As shown in the formula above, the difference between the relative frequency of 10.01 words
per million and absolute frequency of 10 words per corpus is not considerable: 0.01 word.
In other words, the present study focused on the four-word sequences with the 10-occurence
frequency and three-text dispersion, which gave the opportunity to make the analysis more
manageable. It resulted in 565 items before filtering.
3.3.1 Identification of lexical bundles (RQ1)
In order to answer the first research question (What four-word lexical bundles are found in the
corpus?) it was deemed necessary to filter the material, i.e. to remove irrelevant or
unnecessary results for this precise work. The resulting LBs were under extensive quantitative
and qualitative analyses, because “in order to be able to decide what to count” it is necessary
define the material qualitatively (Lindquist & Levin, 2018b, p. 25).
38
3.3.1.1 Analytical steps, exclusion criteria and grouping
In order to define the exact numbers of four-word bundles in the corpus and then analyse
structures and functions of the lexical bundles, a number of including and excluding filters
was applied.
Filter 1. Dispersion
The first cut-off criterion was dispersion. The SketchEngine software has the option to show
in how many texts the sequence appears. All the bundles appeared in less than three texts
were eliminated from the study. In such a case, some of the outliers, or so-called noise
sequences, mentioned above, were eliminated due to the low text-dispersion. However, five
sequences of the same similar nature, namely the notation symbols, appeared in three or more
texts, and consequently were not eliminated automatically. However, as has been mentioned
before, the present study does not take into consideration such symbols and in such a way
disregarded similar entries for the results.
Apart from the noise sequences, the bundles which had to be disregarded due to the low
dispersion turned out to be names of composers and other musicians (rhythm in Bartók's
Contrasts Example, on Bergson's Concept of the Virtual, Robert Cherry and Jennifer Griffith,
in Graham St John, Keith Salley and Daniel T. Shanahan, interview with Bayley, and others.)
and names of pieces of music (Brahms F-minor Clarinet Sonata, Miles Davis Nonet
Manuscripts, the Birth of the Cool).
39
Filter 2. Content-based bundles
The second filter was to eliminate content bundles (Ädel & Erman, 2012). Both studies by
Ädel and Erman (2012) and Salazar (2014) disregarded content-based and topic-specific
bundles due to the aims of their research: the authors were interested in bundles that were
used more generally (or not) and wanted to compare the discipline-specific material to other
types of material in previous research. In the case of this study, it seems reasonable to
eliminate the bundles containing any type of names. A total of 85 removed items included:
The names of the journals (Journal of Electronic Dance Music Culture, Journal of
Music Theory, Journal of Popular Music Studies, Online Journal of the Music Theory,
etc.)
The names of the publishers (University of Minnesota Press, University of Rochester
Press, University of Chicago Press, University of Illinois Press, University of
California Press. )
The names of the institutions of higher education (University of California, University
of New York, University of Texas, Conservatorium of Music, Northern College of
Music, Royal Northern College of Music etc. ).
The titles of the proper articles and the reference articles mentioned in the reference
list or the footnotes of the articles. They could be identified by the capital letter within
the bundle.
Filter 3: Capitalization
The third filter aimed to combine LBs listed twice (or more) due to capitalisation and was
applied to merge the bundles with the initial capital letter which appeared at the beginning of
40
the sentence and then the same sequence with the small letter as in Table 4. Seven examples
of capital/small letters were identified and merged into one.
Table 4. An example of the initial letter variation within the retrieved sequences
sequence frequency
At the same 23
at the same 70
Filter 4: Contractions
In the case of contractions, they were not included in the full list of the sequence results. This
study followed Biber‟s tradition of treating contracted forms as a single and not as two
separated words (Biber et al., 1999, p. 990). In such a case, the two-word contracted
combinations were not considered a type of a lexical bundle composed of three lexical units.
In other words, the sequence don't want to was not included in the results and did not equal
the sequence do not want to. However, it seems relevant to keep track of these phrases as it
may help shed light on the peculiarity of the genre of Music RAs: the following four
contractions were identified in Music research articles despite the fact that those are neither
common nor recommended for use in academic writing (Swales, 1994, p. 18).
Table 5. Two-words contracted combinations identified in the corpus
sequence frequency
don‟t want to 14
I don‟t want 13
I don‟t know 17
I don‟t think 15
41
Some examples may illustrate this peculiar trait of the discipline. These contracted phrases
were used for two main purposes: to refer to someone‟s words (in most cases) and as a
narration from the 1st person singular (in some articles).
A) To refer to someone‟s words in case of interview or quote:
(1) As Finnissy explains: I don't think that it's part of a mechanism to recognise
key centres (doc#25)
(2) or Champion's observation to Redhead (2000: 18): I don't know who said it
now but someone had said 'surely people (doc#64)
(3) I don't want to lose their attention (interview with Bayley, 26 September 2)
(doc#25)
(4) DJs either side of them in the line-up will be playing: If I don't know what a DJ
before me or after me plays, I find out (doc#11)
B) I-pronoun perspective in the narration
(5) had not written about taphephobia, but I don't think he did. (doc#91)
Filter 5: Overlapping bundles
Biber et al. (1999) observed that a number of common lexical bundles can be extended to
longer sequences, resulting in overlapping. According to Chen and Baker (2010),
“overlapping word sequences could inflate the results of quantitative analysis”. They
distinguished between a complete overlap, two four-word bundles which are actually derived
from a single five-word combination, and complete subsumption “referring to a case where
two or more four-word bundles overlap and the occurrences of one of the bundles subsume
those of the other overlapping bundle(s)” (Chen & Baker, 2010, p. 33).
42
In case of overlaps and difficult qualification cases, previous experience was taken into
consideration in this study. The methodology by Salazar (2014, p. 46) proposed eliminating
these cases “to avoid unnecessary repetition and make the list as brief and concise as
possible”. The work of Ädel and Erman (2012) dealt with overlapping bundles by merging
overlapping examples and marking the extensions of a four-word bundle by a plus sign in the
results table. The study of Jalilifar et al.(2016), completely disregarded this criterion.
The present study identified the cases of overlapping lexical bundles, which are listed in
Appendix 2.
3.3.2 Determining the coverage of general bundles over the discipline-specific in the
domain of music (RQ2)
After incorporating all the filters, the following step was to determine the coverage of general
bundles over the discipline-specific one. The full list of music LBs was compared to the ones
by Jalilifar et al. (2016). The author, kindly provided the material of their work, the full set of
results tables. In their work, the researchers created three corpora of two million words each
in three major disciplinary areas: Arts and Humanities, Sciences and Social sciences and.
Also, they identified 566 shared lexical bundles across three disciplinary areas 1 .
In the case of this research, the results of Arts and Humanities of the above mentioned work
were taken into consideration and four-word bundles were compared to our results extracted
from CMRA. This strategy helped to identify general and discipline-specific lexical bundles
1 566 were identified in the table of results, however, in the article the authors mention 661 (Jalilifar &
Ghoreishi, 2018, p. 181)
43
across academic disciplines. The exact match between each one of the bundles in the CMRA
results with any of the ones in the lists of four-word sequences on the Jalilifar et al. (2016)
spread sheet highlighted the general and discipline-specific LBs. Further, an example of the
identification procedure is given: column A identifies the bundle, column B identifies the
number of hits in the Music Corpus, Column C represents the frequency of Arts and
Humanities hits, and Column D is the number of core bundles within three disciplinary areas
(see Table 6).
Table 6. A random sample of the matches count
Lexical bundle
Music Corpus
frequency
(per 1,000,000 words)
Arts and Humanities
frequency
(per 2,000,000 words)
Core bundles across three
disciplinary area corpora
(per 2,000,000 words)
A B C D
at the same time 87 250 607
on the other hand 53 236 761
can be seen in 23 25 86
of the A section 14 0 0
As can be seen from Table 6, the bundles at the same time, on the other hand and can be seen
appeared in all three columns. This meant that these sequences were used both in Music RAs
and in Arts and Humanities RAs collected by Jalilifar et al. (2016). Also, these expressions
occurred in the core bundles across three disciplinary areas and can be considered core
bundles. Those formulaic sequences that occurred only in the Music Corpus column were
considered discipline-specific. When it comes to topic-specific bundles, a manual search was
applied since the understanding of domain-specific vocabulary requires a certain degree of
44
knowledge of the field (Salazar, 2014). By means of detailed qualitative analysis, some topic-
specific bundles were identified, which essentially included music terms or music-related
sequences. Unlike the research by Salazar (2014) which excluded this type of bundle from the
research, we included topic specific bundles into the list of results. First of all, these create a
more complete picture of the formulaic language used by expert writers in the discipline of
music. Secondly, topic-specific bundles have a high degree of pedagogical implication and
could be used as the learning material in teaching ESP.
3.3.3 Structural forms and functions of the lexical bundles (RQ 3 and 4)
After having retrieved the lexical bundles used across the Music RAs, it was interesting to
look into the distribution of the structural forms of the lexical bundles. In order to identify the
structural forms of the lexical bundles, the taxonomy offered in the work by Biber et al.
(1999) was used. This method was also applied by Pan et al. (2016), Kwary et al. (2017) and
Qin (2014), among others.
As mentioned in the methodology section, the taxonomy used for the functional analysis of
lexical bundles was based on a modified version of Hyland‟s (2008b) classification used by
Salazar (2014) and included three major categorizations: research-oriented bundles, text-
oriented bundles and participant-oriented bundles with various sub-categories for each.
Table 7. Functional classification of lexical bundles by Salazar (2014)
Research-oriented
Location Indicate place and
direction
at the site, the tip of
Procedure Indicate events,
actions and methods
the onset of
45
Quantification Indicate measures,
quantities,
proportions and
changes
total volume of
Description Indicate quality,
degree and existence
the appearance of
Grouping Indicate groups,
categories, parts and
order
a wide range of
Text-oriented
Additive Establish additive
links between
elements
in addition to
Comparative Compare and
contrast different
elements
as compared with
Inferential Signal inferences and
conclusions drawn
from data
the results suggest
that
Causative Mark cause and
effect relations
between elements
as a result of
Structuring Organize stretches of
discourse or direct
the reader elsewhere
in text
as described
previously
Framing Situate arguments by
specifying limiting
conditions
in the case of, with
respect to the, in the
presence of
Citation Cite sources and
supporting data
it has been proposed
that
Generalization Signal generally
accepted facts or
statements
little is known about
Objective Introduce the writer‟s
aims
we asked whether
46
Participant-oriented
Stance Convey the writer‟s
attitudes and
evaluations
is likely to
Engagement Address readers
directly
it should be noted
that
Acknowledgments Recognize people or
institutions that have
participated in or
contributed to the
study
would like to
All the bundles were under extensive qualitative and quantitative analysis: the manual
examination of concordance lines was applied in order to check the functions in their full
textual context.
47
4 Results
This section presents the main results based on the four research questions. First, the general
findings on the number of bundles will be presented. In addition, the distribution among
general, discipline-specific and core bundles in the domain of music will be presented.
Finally, the structural and functional distribution of the music lexical bundles will be
discussed.
4.1 Lexical Bundle Identification
The qualitative and quantitative analysis resulted in:
1. 218 lexical bundles in total.
2. 116 of these were shared with the Art and Humanities bundles in Jalilifar et al. (2016)
and were qualified as general lexical bundles.
3. 56 out of 116 general LBs were considered to be core bundles and were shared among
the CMRA and three subject areas (Arts and Humanities, Sciences and Social
Sciences). They are marked in bold in the complete list of LBs given in Appendix 1.
4. 102 turned out to be discipline-specific, as they appeared only in the CMRA. As
previously determined in the Methodology and Material section (Section 3),
discipline-specific bundles are considered to be those which appeared in the CMRA
only and were not shared with the Arts and Humanities corpus. The discipline-specific
bundles are marked in italics in the complete list of LBs (Appendix 1).
5. 20 out of 102 discipline-specific were considered to be topic-specific bundles. They
were music-related sequences like: the first movement of (29 hits), of popular music
studies (26 hits), of the recording studio (18 hits), in the first movement (17 hits), of
48
the first movement (17 hits), a piece of music (16 hits), in the music of (15 hits), the
music of the (15 hits), in the upper voice (14 hits), of the A section (14 hits), in popular
music studies (13 hits), in the music industry (13 hits), in the recording studio (12
hits), of the dance floor (12 hits), from the first movement (12 hits), within popular
music studies (11 hits), electronic dance music culture (11 hits), of the music and (10
hits), structure of the piece (10 hits), the A and B (10 hits).
In terms of overlaps, 13 cases were identified. The full list of overlaps is provided in
Appendix 2.
4.2 Structural Forms
The structural forms used in this study are based on the classification of Biber et al. (1999),
who divided the forms into three main categories: noun-based, preposition-based, and verb-
based bundles. The tabulation of the structural forms is presented in Table 8.
Table 8. The structural forms of the lexical bundles in the Music research articles
Structural forms № of
types
% of
types
№ of
tokens
% of
tokens
Examples
Noun-based noun-phrase with
of-phrase fragment
(1.1)
61 28% 1,153 28% the work of the,
the role of the,
the start of the
noun phrase with
other post-modifier
fragment (1.2)
8 3.7%
203 5% the same way as,
such a way that
complete noun
phrases (1.3)
6 2.7% 119 3% electronic dance
music culture,
the A and B
Total 75 34.4% 1,475 36%
Prepositional-
based
prepositional-based
with embedded -of
72 33% 1,555 38% as a kind of,
49
phrase fragment
(2.1)
in front of the,
at the beginning
of
other prepositional
phrase segments
(2.2)
31 14.2% 500 12% in the same way,
with respect to
the
Total 103 47.2% 2,033 50%
Verb-based
be+noun phrase/
adjective phrase
(3.1)
5 1.8% 80 2% was one of the,
is part of the,
is an example of
passive verb
(3.2)
13 5.9 % 187 4.5% can be heard in,
as shown in
Figure
verb/adjective+that
(3.3)
3 1.4% 39 1% it is clear that,
it is likely that,
should be noted
that
verb/adjective+to
(3.4)
7 3.2% 93 2.2% I would like to,
I use the term
verb phrase with
active verb
(3.5)
9 4.1% 103 2.5% it is possible to,
it is important to
Total: 37 16.9% 502 12.2%
others
(4)
3 1.4% 100 2.4% as well as a2,
as well as the,
at least in part
Total 218 100% 4,110 100%
2 This followed the structural distribution made by Pan et al. (2016, p. 64) who qualified “as well as the” as
Others
50
The noun-based category came second; its 75 hits formed 36% of the total number of the
bundles. The major part is represented by the “noun-phrase with of-phrase fragment”
subcategory (marked as 1.1 in the table of results) giving 61 hits (28%). In terms of overall
frequency, it has resulted in 28.1%. The “noun phrase with other post-modifier fragment”
subcategory (marked as 1.2 in the table of results) has given 8 hits only (3.7%) forming 4.9%
of the total. Six remaining bundles (2.7%) were grouped into the independent subcategory of
“complete noun phrases” (marked as 1.3 in the table of results) due to their nominative nature.
This subcategory has represented 2.9% of all the frequencies. Table 9 provides the examples
of this category with their frequencies while Table 10 gives the overview of the first structural
category, the noun-based phrases.
Table 9. Structural category “complete noun phrase”
Table 10. Noun-based category occurrences and frequencies compared
number
Category: Noun-based Number of types
(%) out of total
218 LBs
Number of tokens
(%) out of total
4110
1.1 noun-phrase with of-phrase
fragment
28% 28%
1.2 noun phrase with other post-
modifier fragment
3.7% 5%
5 other expressions 2.7% 3%
№ Examples Frequency
1 interview with the author 31
2 email to the author 30
3 the A and B 10
4 electronic dance music culture 11
5 the early twentieth century 20
6 the late nineteenth century 17
51
According to the results in Table 10, the category of noun-based phrases is quite balanced in
terms of the number of bundles and their frequencies. The main difference might be seen in
the 1.2 subcategory: it resulted in 5% in types and 3.7% in tokens. This fact may signal that
this category employed a less varied number of expressions more frequently.
The prepositional-based category resulted in 103 bundles (47.2% of types and 50% of tokens)
and formed the most numerous group. The “prepositional-based with embedded -of phrase
fragment” (n=72) (marked as 2.1 in the table of results) achieved the highest rank with 1,533
tokens. This subcategory represented 33% of all the types while “other prepositional phrase
segments” (marked as 2.2 in the table of results) yielded 31 types (12% of all the tokens). As
shown in Table 11, in the case of comparing the frequencies, the 2.1 subcategory turned out to
be more frequently used but employed fewer constructions, while the 2.2 subcategory
demonstrated the opposite: more constructions were used less frequently.
Table 11. Preposition-based category occurrences and frequencies compared
number Category: Preposition-based Number of types (%)
out of total 218 LBs
Number of tokens
(%) out of total 4110
2.1 prepositional-based with embedded -of
phrase fragment 33% 38%
2.2 other prepositional phrase segments 14.2% 12%
The verb-based category has turned out to be the most varied in structures but least
represented in frequencies. 37 verb-based bundles corresponded to 16.9% of the total number
of types. The most numerous group happened to be the passive verb subcategory (marked as
3.2 in the table of results) with 13 bundles (5.9% of total bundles) and it was followed by the
verb phrase with active verb subcategory (marked as 3.5 in the table of results) with nine
52
examples (4.1% of total bundles). The frequency percentage of the above-mentioned
structures can be seen Table 12: passive constructions occurred 187 times per corpus, which is
4.5% of the total while active constructions occurred 103 times resulting in 2.5% of all the
frequencies. The verb-based subcategory “other expressions” was not encountered in the
corpus.
Table 12. Verb-based category occurrences and frequencies compared
number Category: Verb-based Number of types (%)
out of total 218 LBs
Number of tokens
(%) out of total 4110
3.1 be + noun phrase/adjective phrase 2.3% 2%
3.2 passive verb 5.9% 4.5%
3.3. verb/adjective+that 1.4% 1%
3.4 verb/adjective+to
3.2% 2.2%
3.5 verb phrase with active verb
4.1% 2.5%
As shown in Table 12, in spite the varied number of the structures within this category, the
overall frequency is lower in percentage.
The others subcategory resulted in three constructions only (as well as a, as well as the, at
least in part). Another interesting observation in relation to this category is that its total
frequency (100 tokens) turned out to have the same percentage (2.4%) as the 3.5 subcategory
(active verbs) with nine bundles. This finding supports the results by Jalilifar et al. (2016) that
identified that the three-word bundle as well as was the top-used among three-words
sequences with 932 occurrences per Arts and Humanities Corpus, while the sequence as well
as the was the third (with 186 hits per corpus), and together with as well as a (n=38) were
53
considered to be core bundles shared across three subject areas. This gives support to the
important role and a wide use of the above-mentioned constructions in professional academic
writing.
As can be seen from the results, the most common structures of lexical bundles are
prepositional phrases (50% of the total tokens) followed by noun phrases (36% of the total
tokens) and verb phrases (12.2% of the total tokens). The results supported some previous
findings across academic disciplines. For instance, research in Applied Linguistics (Qin,
2014) also found that noun phrases and prepositional phrases dominated and accounted for
slightly more than 50% of LBs. This was similar to the findings in Biber et al. (1999), where
66% of the four-word lexical bundles in academic writing were noun phrases or prepositional
phrases. In the case of Music RAs, the verb-based forms are in minority, which implies the
specific features of the genre: it has a more nomination (descriptive) character of discourse.
4.3 Functional Types
The functional distribution is shown in the table below.
Table 13. The functions of the lexical bundles in the Music research articles
Functions Sub-categories № of
types
% of
types
№ of
tokens
% of
tokens
examples
Research-
oriented, used to
structure
research
activities
time and place 45 20.6
%
1,072 26% the beginning of the,
for the first time,
the end of the,
in the upper voice,
in the world of
procedure
(+manner)
26 11.9
%
443 10.7 % the development of
the, in the analysis of,
as a means to,
through the use of
quantification 5 2.3 % 122 3% is one of the, as one of
the, is part of the
54
description 70 32.1
%
1,145 28% the history of the, the
context of a, the form
of a, the role of the,
an example of a
grouping 14 6.4 % 265 6.4% as part of the, of a
number of, a wide
variety of
Total: 160 73.4% 3,047 74.1%
Text-oriented,
help writers
organize the
text and
develop their
argument
additive 6 2.7% 143
3.4% in addition to the,
in the case of
comparative 3 1.4% 87
2.1% on the other hand
on the one hand
inferential 1 0.45% 11
0.26% that there is no
causative 0 0 0 0 [due] to the fact that
structuring 17 7.8% 255
6.2% can be seen as, be
understood as a, in
order to make, as
shown in Figure
framing 9 4.1% 287
7% in terms of its,
in the context of,
in the case of
citation 4 1.8% 56
1.4% the work of the,
in the work of,
referred to as the
generalization 0 0 0 0
objective 2 0.92% 16
0.4% I would like to,
I use the term
Total: 42 19.2% 855
20.8%
Participant
oriented, that
involve
writers and
readers in the
developing
text
stance 12 5.5% 162
3.9% it is clear that, it is
likely that, as a kind
of, it is important to
engagement 3 1.4% 35
0.85% is important to note,
It should be noted
acknowledgmen
ts
1 0.46% 11
0.26% would like to thank
Total: 16 7.3% 208
5%
Total: 218 100% 4,110 100%
55
More detailed analyses of every functional subcategory are provided below.
4.3.1 Research-oriented bundles
Research-oriented functions “help writers to structure their activities” (Salazar, 2014, p. 167)
and are used to report accounts of the research activities and the world they took place in.
From the perspective of the present study, this category was filled with the bundles that
actually referred to the initial setting of the investigation or the source: they described the
events of the musicians‟ life, referred to pieces of music, or included evidence from the
previous research like questionnaires and responses. This group (n=160 and 3,150 tokens)
turned out to be quite balanced in percentage of types and tokens: 73.4% of types and 74.1%
of tokens.
Following the taxonomy by Salazar (2014), this category was divided into five subcategories:
time and place, procedure, quantification, description and grouping. The order in which these
categories are presented in this work was determined by their frequency order.
The descriptive signals turned out to be the most numerous group (n=70, 1,145 tokens). The
texts contained a large number of narrations, descriptions or references to the musical
literature or life and creativity of musicians. In such a case, this group included all the bundles
that referred to any kind of description of the research setting. It was the most varied group in
structural sense as well. The structural forms were distributed as follows:
56
Table 14. The structural distribution of descriptive signals
Structural type № of types Examples
1.1 noun-phrase with
of-phrase
fragment
33 the onset of the,
the form of a,
second half of the
1.2 noun phrase with
other post-
modifier fragment
3 the extent to which
the relationship between the
1.3 complete noun
phrases
4 interview with the author,
email to the author,
the A and B (strains)
2.1 prepositional-
based with
embedded -of
phrase fragment
21 as a means of
of popular music studies
in the wake of
2.2 other
prepositional
phrase segments
7 in popular music studies
as opposed to the
3.5 verb phrase with
active verb
2 that there is a
which the performer is
It may seem at first glance that some of the bundles should be categorised differently.
However, after a qualitative scrutiny of the concordance lines, their descriptive function was
more evident:
(6) My own involvement in popular music studies has been largely in the field of
popular music and gender and (doc#30)
(7) the latter chord eventually becomes major as a result of the middle ground
semitonal ascent to D♯ in the bass at 7:1 (doc#0)
(8) Example 1, taken from the first movement of Socrate, is typical of Satie's
setting. (doc#71)
(9) and technical perfection at the expense of her emotional welfare (doc#85)
57
The time and place group (n=45, 1,072 tokens) ranked second in number of types but first in
number of tokens. Some typical keywords appeared to be century, beginning, end, middle,
late, early, first, time, top, bottom, front, level, in+, at+. Some most frequent phrases were the
end of the (n=130), at the end of (n=95) and at the same time (n=87). The group included a
number of topic-specific bundles, some illustrations of which can be seen below:
(10) last harmonic sonority in m. 42, an A major chord with C♯ in the upper
voice (doc#15)
(11) also colors a recapitulatory tonic as a non-tonic chord in the first
movement of his Piano Sonata in B♭, D. 960 (doc#0)
The sequence first half of the (n=15) formed part of both general and topic-specific
sequences: first half of the film; first half of the fourteenth century; first half of the nineteenth
century; first half of the phrase; first half of the opening movement; first half of the piece;
first half of the second sequence; first half of the bar; first half of the equal division. The same
was true to say about at the top of (n=11) and (to, from, at, near) the bottom of the (n=11)
sequences, for instance the following examples could be compared:
(12) however the word 'dark' imposes a melodic descent to the bottom of the
stave (Fig. 11) (doc#14)
(13) At the bottom of the page are the missing bars, and an indication of
where (doc#31)
(14) Kowalski also laments the lack of a little café at the top of Mount Eden,
as might have been found in France, (doc#9)
(15) which arrive at the top of his third chorus, clearly paraphrase the
melodic passage (doc#61)
58
In short, the results demonstrated that this group was mainly composed of prepositional
phrases and ranked second after descriptive signals, unlike in the results by Jalilifar et al.
(2016) and Salazar (2014) where the location formulaic sequences appeared in smaller numbers.
It deemed to express the idea that the functional signals of time and place are important in
Music RAs.
The procedure signals (n=26, 433 tokens) are supposed to indicate events, actions and
methods. In case of this study, the sequences with a clear manner function were also included
into this category. Its clear procedural function can be illustrated by the following examples:
(16) This understanding of gesture can be heard in the manipulation of low
frequency oscillators (doc#89)
(17) …mistake made by Strayhorn in the second trumpet part; this can be
heard in the recording if one listens carefully (doc#31)
The grouping subcategory resulted in 14 types (265 tokens). The keywords that helped
identify this function appeared to be number, variety, part, majority, range and great deal.
This group identification turned out to be quite straightforward and did not cause any
ambiguities.
The quantification signals serve to indicate measures. Hyland proposed the constructions as a
wide range of and one of the most to have a quantification functions (2008b, p. 13) while
Salazar (2014, p. 167) qualified the construction a wide range of as a grouping signal. The
present work chose to distinguish these phrases into two different functional categories:
59
grouping and quantification. The latter included five types and 122 tokens. The quantification
signals were as follows:
Table 15. The quantification function of music LBs
structure example tokens
1.1 one of the most 45
2.1 as one of the 30
3.1 is one of the 25
3.1 was one of the 12
3.1 is part of the 10
In short, the category of research-oriented bundles was the most frequent, slightly above 70 %
of both types and tokens of the MCRA. This finding contradicts all the previous results since
in other disciplines text-oriented functions were the biggest group, and research-oriented was
the second. This is finding should be double-checked and examined further.
4.3.2 Text-oriented bundles
Text-oriented functions focus on the text organization and its meaning. It has nine
subcategories, which have been mentioned before, in the Methodology and Material section
of the present study (Section 3). The subcategories have the following frequencies in the
corpus: framing (7%), structuring (6.2%), additive (3.4%), comparative (2.1%), citation
(1.4%), objective (0.4%) and inferential (0.26%) signals. Causative and generalization signals
were not identified in the corpus.
The first and biggest group of the text-oriented category included framing signals which
situate arguments by specifying limiting conditions. It accounted for 287 tokens spread
60
through nine types of bundles. This functional category was the most represented in terms of
tokens in the Music Corpus, similar to the results by Jalilifar and Ghoreishi (2018) who found
that one of the most frequently used functional categories across general and discipline-
specific formulaic sequences was framing.
(18) Given that the metric values are normally shorter in the piano with
respect to the cello part, there are fewer note onsets in the latter, and, as a
consequence, not in every occasion that the piano strikes a new note there is a
note onset in the cello as well. (doc#80)
(19) Exceptions can certainly be found, but they are outside the scope of this
paper (doc#42)
The next frequent signals in the text-oriented category were structuring formulaic sequences.
Structural signals accounted to 17 types spread over 255 tokens. These formulaic sequences
organize the text by providing signals that guide readers through the text (Salazar, 2014).
(20) Further examples can be found in the editorial listings above. (doc#31)
(21) For our present canonic purposes, however, it is more relevant to focus
on the spelling of the chord (doc#56)
(22) For a discussion of the cultural reception of Gesualdo's unusual
contrapuntal style, see Catherine Deutsch's (2013) article (doc#15)
(23) What makes Heppner's breathing all the more interesting for the
purposes of this essay is that his inhalations are strong, quick, and almost
inaudible, even while they clearly are large, athletic breaths (doc#23)
61
Additive signals are used to establish additive (24) or contrastive (25) relations between
elements. This subgroup is represented by six types and 143 tokens, ranking third after
framing and structuring signals. The most frequent and typical additive bundles are as well as
the (n=58), in relation to the (n=36), as well as the (n=32).
(24) In addition to the requisite nights out clubbing, this research involved
qualitative interviews (doc#11)
(25) This is despite the fact that it supports C not Bn, which might suggest F
as its root rather than En (doc#36)
Comparing signals compare and contrast of different elements, as for example, on the other
hand (26), and exclude elements, as in this is not the (27). This subcategory contains three
types only (87 tokens): on the other hand (n=53), on one hand (n=24) and this is not the
(n=10).
(26) A jazz listener, on the other hand, would be quite surprised if Charlie
Parker inserted measure into a chorus (doc#42)
(27) The name, 'melodic death metal', might suggest that the melodic aspects
of the music are more significant than the harmonic aspects, but this is not the
case (doc#65)
Citation signals, which are used to cite research resources and supporting data, accounted for
56 tokens and were distributed over four types: in the work of (n=22), paper presented at the
(n=12), the work of the (n=12), and referred to as the (n=10).
The next text-oriented functions is objective, which introduce the writer‟s aims. The total
entry of the sequence I would like to amounted to 15 hits. However, the analysis of the
62
concordance lines revealed that it served a double function and served as a part of an
acknowledgement phrase I would like to thank (n=11) that will be discussed further in relation
to participant-oriented functions. However, the objective function of the sequence was
identified in six entries and could be illustrated by the following examples:
(28) there is a real vibrancy in popular music performance that I would like
to convey in my own music (doc#27)
(29) The way in which sampling became synonymous with hip-hop and
quotation at the expense of other definitions of the term has led to a number of
problems that I would like to outline and address in this article (doc #109)
Another example of objective signals appeared to be the phrase I use the term (n=10). This
bundle, together with the some similar constructions with an embedded I pronoun found in the
corpus (I would like to, I don't think, I don't know) might signal a unique feature of the genre
of Music RAs: “I can help presenters underline their responsibility in the research”
(Fernández Polo, 2018, p. 15) and highlighting their leading position in the research.
(30) For the purposes of this paper I use the term rather than, for example,
‟computer game‟ or ‟digital game‟ (doc #27)
(31) I use the term super-clubber to denote an individual who contextually
belongs to super-club culture (doc #44)
The remaining text-oriented functions, inferential, were realized through one LB that there is
no with 11 entries. It signalled evidence, or more accurately, a lack of evidence (32).
(32) Compounding the ambiguities of this piece is the fact that there is no
evidence that it is by Mozart at all (doc#62)
Causative and generalization functions, as stated above, were not identified in the corpus.
63
4.3.3. Participant-oriented bundles
Participant-oriented bundles focus on the writer or reader of the text. The taxonomy by
Salazar (2014) proposes three subcategories: stance, engagement, acknowledgements. As in
many other studies (Johnston, 2017), these functions turned out to be the smallest group. In
the present study, it included 16 types and 208 tokens.
The stance subgroup was the most numerous (12 types, 162 tokens), followed by the
engagement signals with three types and 35 tokens. Finally, the acknowledgements subgroup
was represented by one expression with eleven entries in the corpus. Table 16 presents the
percentage of all the groups as well as the LBs.
Table 16. The participle-oriented bundles of music LBs
Function
№ of
types
% of
types
№ of
tokens
% of
tokens
Examples
Stance
12
5.5% 162 3.9% appears to be a
does not appear to,
an important part of,
an integral part of,
of the most important,
as a kind of
it is clear that
it is likely that
it is difficult to
it is important to
it is possible to
at least in part
Engagement
3
1.4% 35 0.85% It is important to note,
Should be noted that,
It should be noted
Acknowledgements
1
0.4% 11 0.25% would like to thank
Total: 16 7.35% 208 5%
64
Another interesting observation was concerning the phrase would like to thank that had eleven
entries and appeared in elevan different texts is that this sequence formed part of the
acknowledgement tradition within Music RAs (illustrated in Figure 8 below)
Figure 8. The demonstration of acknowledgement functions in the corpus
The results show that research-oriented LBs are most frequent (74.1%), followed by text-
organizers (20.8%) and participant organizers with 7% only. This suggests the idea that Music
and Musicology discipline by nature is probably closer to the soft sciences (History,
Literature, and Philosophy) since the bundles have a lot of description-oriented and time and
place research-oriented functions.
65
5 Conclusions
This study has investigated four-word lexical bundles extracted from Music research articles
(RAs). In order to perform this kind of work the Corpus of Music Research Articles (CMRA)
of one million words was created. It included 110 research articles collected from
international music journals of various music subdisciplines; all the articles were written by
native English experts in the field of music.
The present research project was intended to realise four major research questions. The first
research question was to investigate overall frequency of four-word lexical bundles in the
CMRA. The second research question was to identify discipline-specific and general lexical
bundles in the corpus by comparing the results with the ones by Jalilifar et al. (2016). The last
two were to describe the syntactic structures and discourse functions of the extracted four-
word LBs. In this way, the third research question involved identifying the structural
characteristics of the bundles, based on a widely used syntactic classification from Biber et al.
(1999), while the fourth research question involved analysing the overall functions of the
bundles, classifying them into research-oriented, text-oriented or participant-oriented, with
further subcategorizations, based on Salazar (2014).
The following main findings can be summarized as follows: firstly, with respect to the first
research question, 218 LBs were identified; secondly, by comparing the results with the ones
in major subject areas as Arts and Humanities, Sciences and Social Sciences, it was found that
56 out of 218 bundles were core bundles (shared among three different subject areas) and 116
were considered to be general LBs shared with Arts and Humanities only. 102 lexical bundles
66
were considered to be discipline-specific, i.e. unique for CMRA and not shared with any of
the above-mentioned disciplines. 20 out of discipline-specific were considered topic-specific,
containing a specialised music terminology or music-related vocabulary (e.g., the first
movement, in the upper voice, of the A section, structure of the piece).
Thirdly, the quantitative analysis demonstrated that structurally there were predominantly
prepositional phrases (47.8% of the total) and noun phrases (34.4% of the total) in the corpus,
while verb phrases yielded around 12%. If these results are to be compared to some previous
findings across academic disciplines (see Table 2) it can be seen that Music RAs used the
biggest number of prepositional phrases, even surpassing Medicine with its 44.5% (Jalali et
al., 2015). Music RAs employed almost the same number of noun phrases as the
Telecommunication discipline (36.4%) (Pan et al., 2016); however, featuring the smallest
percentage of verb phrases (all the other disciplines demonstrated at least over 25% of verb
phrases). This finding is related to the idea formulated by Biber et al. (1999, p. 992) that most
lexical bundles in academic prose are “building blocks for extended noun phrases or
prepositional phrases” while “verbal and clausal structural units” are most typical of
conversation .
Regarding the forth research question, the main feature of MRAs turned out to be discourse
functions of lexical bundles. While the majority of academic disciplines used text-oriented
bundles (see Table 2), the Corpus of Music Research Articles used research-oriented bundles
(with slightly more than 70%). The major functional subcategories turned out to be
descriptive signals, amounting to 28% (the history of the, the role of the, the example of the),
67
and time and place signals, amounting to 26% (the beginning of the, the end of the, in the
world of, for the first time). The category of text-oriented signals amounted to almost 21% of
the total. Participant-oriented bundles were the least frequently used category (5%).
By performing qualitative analysis, it has been possible to find ways in which Music RAs are
unique as a discipline. Music RAs do not avoid the use of contractions or I-perspective
narration. Furthermore, it has multiple references to music pieces (e.g., F-minor Clarinet
Sonata, Miles Davis Nonet Manuscripts), composers and other representatives of the music
industry (e.g., rhythm in Bartok’s Contrasts Example) as part of its specialist terminology.
However, there are some limitations in the present study. The first limitation lies in the
material selection, i.e. three-word bundles were not considered for this research, though they
have been proved to be an important part of academic formulas (Simpson-Vlach & Ellis,
2010). Further, the second limitation concerns the methodology used in the present research.
Qualitative analysis of the discourse functions might be quite subjective and could rely on the
author‟s understanding of the material. In this type of analysis, it would be desirable to have
two researchers qualifying the functions in order to overcome bias by contrasting them.
Lastly, the third limitation is that the results cannot be taken for “absolute truth” but should be
considered as the reflection of the material used in the particular corpus created for this
particular study.
The findings of this work have demonstrated a big potential for pedagogical application.
Together with genre analysis of moves and rhetorical structure of Music research articles, this
68
lexical bundles analysis can help both non-native professionals and novice language users
improve their writing proficiency in English by raising awareness of introducing the most
frequent or typical formulaic sequences into their written discourse. Undergraduate students
of musical higher educational institutions could also benefit from the results of the present
work. Phrase-based lexical drills might employ this knowledge and bring it into teaching
practices. As proposed by Johnston (2017, p. 69), teachers could create a cloze-type activity
where these bundles are removed from the text and students are asked to replace them in the
text or they could then complete a guided fill-in-the-gap activity.
In terms of recommendations for the further research, it would be interesting to compare the
use of formulaic language in different music subdisciplines (e.g., jazz, history of music, opera
singing, etc.). Moreover, it would be worth investigating how different subfields are included
in the discipline of Music and Musicology and the way they relate to each other from the
point of view of formulaicity or idiomaticity. Additionally, another way of developing the
present study could be looking at music terminology used by different discourse communities
or in different genres or registers.
In closing, this study has provided new insights in understanding the discipline-specific
discourse of Music research articles and as a basis for doing further corpus-based research in
written academic discourse and EAP.
69
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Appendix 1. The Full List of Lexical Bundles in the CMRA
116 LBs in normal-face: general lexical bundles shared with the Art
and Humanities
102 LBs in italics-face: discipline-specific LB appeared only in
CMRA
56 LBs in bold-face: core LB shared among CMRA and three other
subject areas
Frequency
the end of the 130
at the end of 95
in the context of 90
at the same time 87
the beginning of the 72
as well as the 58
in the case of 56
the ways in which 54
on the other hand 53
at the University of 50
one of the most 45
the way in which 43
in terms of the 39
of the twentieth century 39
as part of a 38
in the form of 38
at the beginning of 38
the extent to which 37
in relation to the 36
as part of the 36
the rest of the 35
the use of the 34
as a means of 33
as well as a 32
75
interview with the author 31
the start of the 31
the role of the 31
email to the author 30
the context of the 30
as one of the 30
it is important to 30
the first movement of 29
as a result of 29
of popular music studies 26
can be found in 26
is one of the 25
on the one hand 24
as an example of 24
in a way that 23
a wide range of 23
can be seen in 23
in the work of 22
in such a way 22
to be able to 22
in the middle of 22
over the course of 22
at the heart of 22
within the context of 21
the second half of 20
the sound of the 20
the structure of the 20
the fact that the 20
the turn of the 20
the early twentieth century 20
for the first time 20
in the development of 19
the middle of the 19
76
as a way of 18
the course of the 18
the nature of the 18
in the sense that 18
of a number of 18
a great deal of 18
of the recording studio 18
for the sake of 17
of the first movement 17
second half of the 17
the relationship between the 17
on the basis of 17
in a variety of 17
the same way as 17
be found in the 17
in the first movement 17
of the nineteenth century 17
the late nineteenth century 17
a piece of music 16
the first half of 16
as a form of 16
on the part of 16
the level of the 16
that there is a 16
in the same way 16
at the time of 16
an example of a 15
the music of the 15
as opposed to the 15
the part of the 15
from the perspective of 15
a part of the 15
in an attempt to 15
77
a result of the 15
an important part of 15
can be seen as 15
for a discussion of 15
in the music of 15
first half of the 15
despite the fact that 14
of the a section 14
the return of the 14
should be noted that 14
through a process of 14
through the use of 14
to the development of 14
with the assistance of 14
with the exception of 14
to do with the 14
in the history of 14
at the start of 14
in the upper voice 14
in popular music studies 13
the opening of the 13
about the nature of 13
at the expense of 13
in the wake of 13
the form of a 13
to the use of 13
the development of a 13
such a way that 13
it is likely that 13
an integral part of 13
of the most important 13
it is difficult to 13
it is possible to 13
78
as shown in figure 13
be seen in the 13
in order to make 13
the purposes of this 13
can be understood as 13
in the music industry 13
the beginning of a 13
the end of a 13
to the end of 13
at the level of 13
paper presented at the 12
the work of the 12
from the first movement 12
in the face of 12
of the dance floor 12
the idea of the 12
in terms of its 12
with respect to the 12
the majority of the 12
in a number of 12
and the use of 12
way in which the 12
was one of the 12
it is clear that 12
be understood as a 12
be seen as a 12
to the fact that 12
in the recording studio 12
in the first place 12
in the process of 12
at a time when 12
in the world of 12
would like to thank 11
79
in addition to the 11
this is not to 11
in the sense of 11
the centre of the 11
the onset of the 11
the context of a 11
in response to the 11
of the relationship between 11
the form of a 11
electronic dance music culture 11
it should be noted 11
of some of the 11
that there is no 11
as a means to 11
the development of the 11
in the manner of 11
of the ways in 11
is an example of 11
to focus on the 11
at the centre of 11
at the top of 11
beginning of the recapitulation 11
in a position to 11
the bottom of the 11
within popular music studies 11
which the performer is 10
referred to as the 10
this is not the 10
and its relationship to 10
of the music and 10
structure of the piece 10
the a and b 10
the experience of the 10
80
the field of popular 10
the influence of the 10
in the use of 10
an example of the 10
and the role of 10
the history of the 10
the case of the 10
in the absence of 10
with the idea of 10
is important to note 10
the scope of this 10
one of the first 10
a wide variety of 10
the first of the 10
I use the term 10
can be heard in 10
in the analysis of 10
the manner in which 10
is part of the 10
appears to be a 10
at least in part 10
does not appear to 10
as a kind of 10
in the long term 10
in the nineteenth century 10
in the performance of 10
on the dance floor 10
in front of the 10
I would like to 6
81
Appendix 2. The List of Overlaps in the CMRA
Example Initial frequency
1 at the beginning of 45
the beginning of the 75
the beginning of a 13
2 at the end of 102
the end of the 138
the end of a 15
to the end of 13
3 the start of the 31
at the start of 14
4 in the music of 15
the music of the 15
5 second half of the 17
the second half of 20
6 the first half of 16
first half of the 15
7 as an example of 24
an example of a 15
8 be found in the 17
be seen as a 12
be understood as a 12
9 can be found in 26
can be seen as 15
82
can be seen in 23
can be understood as 13
10 to the fact that 12
despite the fact that 14
the fact that the 20
11 in such a way 22
such a way that 13
12 in the work of 22
the work of the 12
13 would like to thank 11
I would like to 15
83
Appendix 3. The list of the RAs used for the CMRA
Document number in
CMRA
Articles [consulted: 2021-05-25]
Doc#0 Chandler, O. (2020). A Diminished-Seventh Bassbrechung: Tonal
Ambiguity and the Prolongation of Function in Edward Elgar‟s String
Quartet, 1st Movement. GAMUT, 9, 3-29. Retrieved from
https://trace.tennessee.edu/gamut/vol9/iss1/2/
Doc#01 Selleck, J. (2017). Back to the Future: The Proud Legacy of
Melbourne‟s Colonial Women Composers. Context, 42, 1-22.
Retrieved from https://contextjournal.music.unimelb.edu.au/no-42-
2017/
Doc#02 Graham, St. J. (2009). Neotrance and the Psychedelic Festival.
Dancecult: Journal of Electronic Dance Music Culture, 1(1), 35-64.
Retrieved from
https://dj.dancecult.net/index.php/dancecult/article/view/270
Doc#03 Evans, C. P. (2020). The Politics of Music: Women‟s Music
Education in the United States in the late 18th
Century. Current
Musicology, 105, 21-41. Retrieved from
https://doi.org/10.7916/cm.v0i105.5401
Doc#04 Firth, S. (2010). Analysing live music in the UK: Findings One Year
into a Three Year Research Project. Journal of the International
Association for the Study of Popular Music, 1, 1-30. Retrieved from
https://iaspmjournal.net/index.php/IASPM_Journal/article/view/335
Doc#05 Lovell, J. (2011). Out of the Ordinary: Andrew Hill‟s “Verona Rag”.
Journal of Jazz Studies, 7 (1), 47–72. Retrieved from
https://doi.org/10.14713/jjs.v7i1.4
Doc#06 McKenry, T. (2013). From Overt to Covert: The Changing Role of
Cultural Commentary in Australian Operatic Repertoire 1990-2009.
Journal of Music Research Online, 4, 1-12. Retrieved from
http://www.jmro.org.au/index.php/mca2/article/view/84/32
84
Doc#07 MacCutcheon, D., Greasley, A. E., & Elliott, M. T. (2016).
Investigating the Value of DJ Performance for Contemporary Music
Education and Sensorimotor Synchronisation (SMS) Abilities.
Dancecult: Journal of Electronic Dance Music Culture, 8(1), 46–72.
Retrieved from
https://dj.dancecult.net/index.php/dancecult/article/view/557
Doc#08 Chandler, O. (2020). A Diminished-Seventh Bassbrechung: Tonal
Ambiguity and the Prolongation of Function in Edward Elgar‟s String
Quartet, 1st Movement. GAMUT, 9, 3-29. Retrieved from
https://trace.tennessee.edu/gamut/vol9/iss1/2/
Doc#09 Murphy, K. (2017). A Counterpoint of Critical Voices: Travelling
Musicians in Colonial New Zealand. Context, 42, 23–35. Retrieved
from https://contextjournal.music.unimelb.edu.au/no-42-2017/
Doc#10 Graham, St. J. (2009). Neotrance and the Psychedelic Festival.
Dancecult: Journal of Electronic Dance Music Culture, 1(1), 35-64.
Retrieved from
https://dj.dancecult.net/index.php/dancecult/article/view/270
Doc#11 Montano, E. (2009). DJ Culture in the Commercial Sydney Dance
Music Scene. Dancecult: Journal of Electronic Dance Music Culture,
1(1), 81-93. Retrieved from
https://dj.dancecult.net/index.php/dancecult/article/view/272
Doc#12 Green, E. (2011). “Harlem Air Shaft”: A True Programmatic
Composition? Journal of Jazz Studies, 7(1), 28–46. Retrieved from
https://doi.org/10.14713/jjs.v7i1.9
Doc#13 Maloy, L. (2010). „Staying Alive in Da Club: The Illegality and
Hyperreality of Mashups. Journal of the International Association for
the Study of Popular Music, 2, 1-20. Retrieved from
https://iaspmjournal.net/index.php/IASPM_Journal/article/view/372
Doc#14 McMahon, P (2017). Darkness and Light: Handel‟s Rhetorical Vocal
Writing in the English Oratorio Samson. Journal of Music Research
Online, 8, 1-27. Retrieved from
http://www.jmro.org.au/index.php/mca2/article/view/198
85
Doc#15 Lively, M. & Bleile M. L. (2020). Gesualdo‟s Moro Lasso and the
Freudian Repetition Compulsion. GAMUT, 9, 1-38. Retrieved from
https://trace.tennessee.edu/gamut/vol9/iss1/4/
Doc#16 Glauert, A. (2013). “Do you know the land?” Unfolding the secrets of
the lyric in performance. Music Performance Research, 6, 68-96.
Retrieved from http://musicperformanceresearch.org/volume-6/
Doc#17 Lawrence, T. (2016). Life and Death on the Pulse Dance Floor:
Transglocal Politics and the Erasure of the Latinx in the History of
Queer Dance. Dancecult: Journal of Electronic Dance Music
Culture, 8(1), 1–25. Retrieved from
https://dj.dancecult.net/index.php/dancecult/article/view/906
Doc#18 Callaghan, A. (2017). Realism and the „Inaudible‟ Score for
Spotlight. Context, 42. 53–66. Retrieved from
https://contextjournal.music.unimelb.edu.au/no-42-2017/
Doc#19 Graham, St. J. (2010). Making a Noise – Making a Difference:
Techno-Punk and Terra-ism. Dancecult: Journal of Electronic Dance
Music Culture, 1(2), 1-28. Retrieved from
https://dj.dancecult.net/index.php/dancecult/article/view/285
Doc#20 Morey, J. (2013).Copyright Management and its Effect on the
Sampling Practice of UK Dance Music Producers. Journal of the
International Association for the Study of Popular Music, 3(1), 48-
62. Retrieved from
https://iaspmjournal.net/index.php/IASPM_Journal/article/view/589
Doc#21 Jeffreys, C. (2018). Johannes de Grocheio, the Ars musice and the
Transformation of Chant Theory in the late Thirteenth Century.
Journal of Music Research Online, 9, 1-12. Retrieved from
http://www.jmro.org.au/index.php/mca2/article/view/218
Doc#22 McGowan, J. (2011). Psychoacoustic Foundations of Contextual
Harmonic Stability in Jazz Piano Voicings. Journal of Jazz Studies,
7(2), 156-191. Retrieved from https://doi.org/10.14713/jjs.v7i2.13
86
Doc#23 Parr, S. M. (2019).Wagnerian Singing and the Limits of Vocal
Pedagogy. Current Musicology, 105, 56-74. Retrieved from
https://doi.org/10.7916/cm.v0i105.5403
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Doc#42 Love, S. (2012). An Approach to Phrase Rhythm in Jazz. Journal of
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Doc#54 Teo, Y. (2018). Hybridising the Schenkerian Method: A Selected
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Doc#64 Morrison, S.A. (2014). “Surely people who go clubbing don‟t read”:
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Doc#69 Forte, A. (2011).The Development of Diminutions in American Jazz.
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Doc#70 Carr-Richardson, A. (2011). A Study of Donald Grantham‟s Fantasy
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Doc#73 Wilford, S. (2015). “In Our Culture, Poets Have More Power than
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in Jazz Worlds: A Case Study of Joe Farrell‟s “Moon Germs.”
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Doc#76 Osborn, B. (2010). Beats That Commute: Algebraic And Kinesthetic
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Doc#84 Lashua, B. D. (2016). Producing Music, Producing Myth? Creativity
in Recording Studios. Journal of the International Association for the
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Doc#85 Dobson, M.C. (2010). Performing your self? Autonomy and self‐
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Doc#88 Doll, C. (2009). Transformation in Rock Harmony: An Explanatory
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Doc#93 Martin, T. (2017). Making Music in Bankstown: Responding to Place
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Doc#97 Bennet, T. (2018). “The Whole Feminist Taking-your-Clothes-off
Thing”: Negotiating the Critique of Gender Inequality in UK Music
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Doc#104 Smith, G. (2010/2011). The Gendered Voice of Australian Country
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Doc#106 Waters, J. (2010/2011). Alan Bush‟s Byron Symphony and Anti-
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