The legal system includes civil and criminal institutions. The criminal justice system’s concern...

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Transcript of The legal system includes civil and criminal institutions. The criminal justice system’s concern...

Page 1: The legal system includes civil and criminal institutions.  The criminal justice system’s concern with punishment distinguishes it from civil institutions.
Page 2: The legal system includes civil and criminal institutions.  The criminal justice system’s concern with punishment distinguishes it from civil institutions.

The legal system includes civil and criminal institutions.

The criminal justice system’s concern with punishment distinguishes it from civil institutions.

Can punishment be used to predict the appropriate institution to apply?

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Special rights are afforded in the criminal justice system because legal punishment is at stake.

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1. Hard Treatment2. inflicted for a violation of legal rules3. on the actual or supposed violator of the

rules4. imposed by people other than the violator5. who have the authority to do so under the

rules of the legal system.(H.L.A. Hart, 1968; Feinberg & Gross, 1995)

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6. Punishment expresses the community’s condemnation (Hart, 1958; Feinberg, 1995).

What is meant by condemnation?• An expression of “resentment and

indignation,” fused with disapproving judgment (Feinberg, 1995).

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Consider a mobster who “punishes” a snitch who testifies against him by murdering the snitch. We might say that the mobster feels resentment, hatred, etc. toward the snitch.

But this is not the condemnation that the criminal justice system expresses when it punishes. We must add a measure of judgment.

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Consider Dirty Harry, a police officer who punishes suspects through the use of force (often deadly).

His actions may express the same feelings of resentment, hatred, etc. that the mobster’s actions expressed.

We have also added a measure of judgment—if Dirty Harry’s punishment is only imposed on lawbreakers.

But we must add a bit more judgment to approximate the sort of punishment imposed by the criminal justice system.

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Arguably the criminal justice system imposes punishment that expresses the same emotions referred to earlier, but incorporates restraining judgment (e.g., proportional punishment directed only at lawbreakers, in contrast to Dirty Harry and Capone).

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The criminal justice system incorporates the retributive concept of guilt.

It follows that condemnation is expressed toward a person only if she is guilty.

Note that it is an empirical question whether condemnation is actually expressed toward the guilty.

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Insanity is an affirmative defense. No punishment if successful. There may still be a deprivation of

liberty, however, through the institution of civil commitment.

Note that this deprivation of liberty is not punishment, even though it follows a violation of the criminal law.

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Attitudes toward the insanity defense (e.g. Louden & Skeem, 2007; Poulson et al., 1997).

The type of mental illness at issue (e.g. Roberts et al., 1987).

The severity of the crime (e.g. Bailis et al., 1995).

The availability of different verdict options (GBMI) (e.g. Poulson, 1990).

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H1: Condemnation scores will differ across cases based on different mental illnesses.

H2: Condemnation scores will be higher when the charged offense is more severe.

H3: Condemnation scores will predict verdict decisions, and attitudes will have no independent contribution to the prediction model.

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150 undergraduate students completed a packet that included:› Four hypothetical case scenarios

(vignettes)› A measure of insanity defense attitudes› Sets of dependent measures for each

hypothetical case

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Vignettes described assaults committed by defendants suffering from:Seizure disorder

(low condemnation)

PTSD/battered child syndromeParanoid schizophreniaAntisocial personality disorder

(high condemnation)

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The vignettes varied between groups by severity of harm (assault causing minor injuries vs. fatal injuries) and the presence/absence of the GBMI verdict option.

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Modified version of Hans (1986) Insanity Defense Attitudes Scale (16 items rated on 7-point Likert scale)› E.g. “The insane should be treated rather than

punished.”› “Punishment just does not work on the insane.”› α = .794

Pearce (2008) Condemnation Scale (23 items rated on 7-point Likert scale)› E.g. “I feel hatred toward the defendant.”› “Sentencing the defendant to prison would balance

society’s need to express revenge toward him with rational judgment”

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The reliability of the condemnation scale was good for all vignettes:› Seizure disorder: α

= .936› PTSD/battered child syndrome: α = .950› Paranoid schizophrenia: α = .944› Antisocial personality disorder: α = .940

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(Summary of main effects: Seizure < schiz = PTSD < Antisoc.)

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Summary of main effect: assault < homicide

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Condemnation scores were often predictive of verdict decisions, and attitudes had only a limited independent contribution to the models.› Due to a wide variety in verdict frequency

patterns across diagnoses, separate logistic regression analyses were conducted for each diagnosis.

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Predicted Frequencies

Observed Frequencies

NGRI GuiltyPercentage Correct

NGRI 66 1 98.5

Guilty 4 2 33.3

Total Percentage Correct

93.2

Note: Constant-only model accuracy = 91.8%

The classification table shows the results of a model that includes insanity defense attitudes and condemnation as predictors

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Predicted Frequencies

Observed Frequencies

NGRI GuiltyPercentage Correct

NGRI 66 1 98.5

Guilty 4 2 33.3

Total Percentage Correct

93.2

Note: Constant-only model accuracy =

91.8%

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .148 .064 5.332 1 .021 1.160

Attitudes .139 .070 3.983 1 .046 1.149

Constant -24.0

8.45 8.072 1 .004 - -

Cox & Snell R2 = .234, Nagelkerke R2 = .541

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .148 .064 5.332 1 .021 1.160

Attitudes .139 .070 3.983 1 .046 1.149

Constant -24.0

8.45 8.072 1 .004 - -

Cox & Snell R2 = .234, Nagelkerke R2 = .541

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .148 .064 5.332 1 .021 1.160

Attitudes .139 .070 3.983 1 .046 1.149

Constant -24.0

8.45 8.072 1 .004 - -

Cox & Snell R2 = .234, Nagelkerke R2 = .541

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .148 .064 5.332 1 .021 1.160

Attitudes .139 .070 3.983 1 .046 1.149

Constant -24.0

8.45 8.072 1 .004 - -

Cox & Snell R2 = .234, Nagelkerke R2 = .541A word about logistic regression: the best measure of affect size is the odds ratio. This odds ratio (1.16) means that for each single-point increase on the condemnation scale, the odds of obtaining a guilty verdict (as opposed to an NGRI verdict) increase by 16%.

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Predicted Frequencies

Observed Frequencies

NGRI GuiltyPercentage Correct

NGRI 10 20 33.3

Guilty 8 34 81.0

Total Percentage Correct

61.1

Note: Constant-only model accuracy = 58.3%

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Predicted Frequencies

Observed Frequencies

NGRI GuiltyPercentage Correct

NGRI 10 20 33.3

Guilty 8 34 81.0

Total Percentage Correct

61.1

Note: Constant-only model accuracy =

58.3%

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .034 .015 5.604 1 .018 1.035

Attitudes .018 .029 .389 1 .533 1.018

Constant -3.75

2.01 3.152 1 .076 - -

Cox & Snell R2 = .109, Nagelkerke R2 = .147

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .034 .015 5.604 1 .018 1.035

Attitudes .018 .029 .389 1 .533 1.018

Constant -3.75

2.01 3.152 1 .076 - -

Cox & Snell R2 = .109, Nagelkerke R2 = .147

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Predicted Frequencies

Observed Frequencies

NGRI GuiltyPercentage Correct

NGRI 50 3 94.3

Guilty 11 9 45.0

Total Percentage Correct

80.8

Note: Constant-only model accuracy = 72.6%

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Predicted Frequencies

Observed Frequencies

NGRI GuiltyPercentage Correct

NGRI 50 3 94.3

Guilty 11 9 45.0

Total Percentage Correct

80.8

Note: Constant-only model accuracy =

72.6%

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .058 .025 5.345 1 .021 1.059

Attitudes .076 .039 3.763 1 .052 1.079

Constant -10.9

3.40 10.26 1 .001 - -

Cox & Snell R2 = .191, Nagelkerke R2 = .276

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .058 .025 5.345 1 .021 1.059

Attitudes .076 .039 3.763 1 .052 1.079

Constant -10.9

3.40 10.26 1 .001 - -

Cox & Snell R2 = .191, Nagelkerke R2 = .276

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97% of participants returned a verdict of “guilty.” Condemnation scores could not predict verdicts better than the constant-only model. (X2 = 0.834, p = .361)

But . . . When GBMI was a verdict option, 2

participants returned verdicts of NGRI, 13 of GBMI, and 61 of guilty.

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Predicted Frequencies

Observed Frequencies

GBMI GuiltyPercentage Correct

GBMI 1 12 7.7

Guilty 0 61 100

Total Percentage Correct

83.8

Note: Constant-only model accuracy = 82.4%

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Predicted Frequencies

Observed Frequencies

GBMI GuiltyPercentage Correct

GBMI 1 12 7.7

Guilty 0 61 100

Total Percentage Correct

83.8

Note: Constant-only model accuracy =

82.4%

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .053 .024 4.995 1 .025 1.054

Attitudes .056 .042 1.798 1 .180 1.058

Constant -7.62

4.03 3.572 1 .059 - -

Cox & Snell R2 = .106, Nagelkerke R2 = .176

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .053 .024 4.995 1 .025 1.054

Attitudes .056 .042 1.798 1 .180 1.058

Constant -7.62

4.03 3.572 1 .059 - -

Cox & Snell R2 = .106, Nagelkerke R2 = .176

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Condemnation scale seems to be a reliable measure.

Condemnation scores differ across diagnoses in the expected manner.

Higher condemnation scores are associated with more severe crimes.

Condemnation can predict verdicts.

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Juvenile justice systems were formed to rehabilitate young offenders.

Generally, they are not punitive— --which means that rights and

procedures may differ. Certain juveniles are eligible to proceed

in either system.

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Clinicians asked to make recommendations tend to emphasize dangerousness and treatment amenability, but de-emphasize accountability in their reports to the court (Salekin et al., 2001)

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H1: Condemnation scores will be significantly higher when the youth’s offense is severe and premeditated

H2: Condemnation scores will predict charging decisions, such that higher scores will be associated with decisions to try the youth in criminal court

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235 attorneys (90 defense attorneys, 134 prosecutors) completed a packet that included:› One of four vignettes› Pearce (2008) condemnation scale (14

items, α = .794) Vignettes described assaults by youths

› The vignettes varied between groups based on the severity of the assault and whether the assault was premeditated

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H1: There was no interaction between offense severity and premeditation as they relate to condemnation scores, no main effects for severity or premeditation.

H2: Binary logistic regression analysis indicates that condemnation scores are predictive of charging decisions

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Predicted Frequencies

Observed Frequencies

Juvenile CriminalPercentage Correct

Juvenile 123 12 91.1

Criminal 50 19 27.5

Total Percentage Correct

69.6

Note: Constant-only model accuracy = 66.2%

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Predicted Frequencies

Observed Frequencies

Juvenile CriminalPercentage Correct

Juvenile 123 12 91.1

Criminal 50 19 27.5

Total Percentage Correct

69.6

Note: Constant-only model accuracy =

66.2%

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .059 .013 19.62 1 .001 1.061

Constant -2.92

.539 29.45 1 .001 - -

Cox & Snell R2 = .106, Nagelkerke R2 = .147

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Binary Logistic Regression Analysis—condemnation + attitudes

Predictor ß S.E. ß Wald X2 df p eß (odds ratio)

Condemnation .059 .013 19.62 1 .001 1.061

Constant -2.92

.539 29.45 1 .001 - -

Cox & Snell R2 = .106, Nagelkerke R2 = .147

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The condemnation scale was a reliable measure.

Although condemnation scores were not significantly related to offense severity and premeditation, the manipulations may have been too weak.

A predictive model based on condemnation appears to improve classification accuracy over a constant only model when charging decisions are analyzed.

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Condemnation can be operationalized and used to predict decisions involving punishment.

It appears that insanity decisions and juvenile charging decisions are consistent with a basic reason for maintaining civil and criminal institutions.

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Condemnation may have application in many other contexts:› Criminal sentencing› Application of death penalty› Punitive damages awards› Preliminary work has been done testing the

concept in the context of sex offender commitments

The emotional and judgmental components of condemnation may merit further exploration

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Bob Schopp, Rich Wiener, Cal Garbin, Marty Gardner, Hon. Richard Kopf, Eve Brank, Jennifer Groscup, Mario Scalora, Brian Bornstein, Cynthia Willis-Esqueda, Steve Penrod, Aletha Claussen-Schulz, Bridget Faimon, Stephanie Kucera, Brook Glassman, Heather Easter, Amber Boots, Lori Ketteler, Angela Cox, and Lindsey Wylie.