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Stories in action(www.GlobalGiving.org/stories)

24,400 stories collected in 2011>500 locationsStories about “community efforts”http://batchgeo.com/map/350267f319cf19cedfdf447fc0afa5f8(colors are arbitrary --- distance from Nairobi)

our GG Storytelling process…

1. Build a network of NGOs --- took us 5 years2. Invite partners to find a handful of local young people who

want to become story collectors --- a dozen scribes / town3. Visit and train groups of scribes across the region --- over

2000 people in 20114. Collect stories on paper monthly, transcribe to web5. Analyze stories for patterns, lessons, & overall messages ---

SenseMaker® and other visual tools6. Improve story quality though feedback and meta-analysis7. Regularly deliver feedback to NGOs --- community feedback

sessions every 3-6 months8. SMS feedback & news to storytellers --- just starting9. Meta-Analyze all of the above in order to learn about our

network, and promote organizations with high curiosity --- prerequisite to problem solving & innovation

Community feedback reaches donors and local organizations

Might better align projects with needs

Incentive: Easier Evaluations?

$300 billion /year in P2P aid

Technology-aided feedback loops

Existing aid feedback loopsPolicy oriented

Slow to adapt

Local people not involved

Incentive: Helping donor countries’ economies

$127 billion / year in ODA

Nuts & bolts of the method

Paper collection

Training• collect 20-30 / month --- get 12 cents per story• get 2 stories per person --- for a “within subjects” baseline• start with people you know / comfortable talking to

start

end

More examples on my blog:chewychunks.wordpress.com

Analysis toolsComparing patterns in groups of stories

Geo Mapping

Community of 400 NGOs in 3 clusters

Face to face meetings

F GM

Seeing story themes

For comparing interpretations and getting a reality check

SenseMaker® GephiMapping relationships

Story search & download

SenseMaker®versus other methods

Requires:Lots of narrative fragmentsSignification framework (questionnaire about the story told)

  (quasi) experimental methods narratives (case studies, MSC) SenseMaker® based

1.    Outputs answer about which intervention changed which variables most in a particular context

in-depth experiences that explains a change process

how different people experience change process; type of changes /behaviours/ values

2.      Type of study and frequency

one-off comparison; usually no intermediate data points

process analysis; one-off study or ongoing

one-off study or ongoing monitoring of emerging patterns (with feedback loops)

3.     Organising principle for question focus

comparing specific interventions, anticipated observable change variables – before/after and with/without

change process, context, specific changes and their value (not pre-determined)

values, behaviours, beliefs that are the focus of change

4 Type of data on which analysis is based

quantitative variables that either count or are relative score (0 to 10); sometimes qualitative studies to explain why

selection of in-depth experiences in context; usually no quantitative comparisons

quantified narratives from people (nuanced knowledge); context provides meaning; numbers enable seeing of trends

5    Numbers summaries people’s opinions or measurable variables; strong focus on average effect; no focus on context-specific insights

no averaging; few if any quantities; sometimes limited cases assumed representative

identifies emerging patterns based on fragments of people’s experiences; moving between numbers and stories gives contextualised statistics

6.   Rigour defined by

statistically validated causal attribution; counterfactual

quality of in-depth study; probing; explaining

diversity and number of stories; ability to infer from nuanced analysis; utility of patterns for action

A Aggregation easy via standardised responses rare as low ‘n’ to aggregate; very time-consuming, external interpretation

easy through relative positioning on triads/dyads

Examples of analysisRoot causes of a complex social problem (drilling down)

Looking at 1617 “school fees” stories: Those tagged with “need” + “failure” are coming from women.

From 1784 “hiv/aids” stories: Those tagged “security” + “family” and not about any organization are about rape or sexual assault, mostly from women.

Licensed SenseMaker® software

Examples of analysisRoot causes of a complex social problem (rape)

Mrembo girls talk about… Sita Kimya men talk about…Comparing story sets reveals different emphasis

Examples of analysisReveal hidden / unconscious biases among storytellers

Licensed SenseMaker® software

Kenya Uganda

Examples of analysisWhat are people talking about in a community?Stretching SenseMaker® to visualize story characteristics

Licensed SenseMaker® software

westandwiththe99percent.tumblr.com

Examples of analysisWhat are people talking about in a community (phrases)?

Network diagrams are generated with python / networkx and visualized with Gephi (all free software)

Examples of analysisWhat are people talking about in a community?

http://www.globalgivingcommunity.com/circle_4.php

Examples of analysisWho is / ought to be working with whom?

Network diagrams are generated with python / networkx and visualized with Gephi (all free software)

Full NGO network derived from stories

core NGOs

Examples of analysisWho is / ought to be working with whom?

Organizations’ perspective generated during NGO meetings

Our world is full of complex problems…

We need non-linear visualization techniques to understand them.