Download presentation

Presentation is loading. Please wait.

Published byHolly Norford Modified over 2 years ago

2
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?

3
Legal System Criminal Justice System Civil Institutions Civil Commitment Juvenile Justice* Civil Law (Tort/Contract) Special rights are afforded in the criminal justice system because legal punishment is at stake.

4
1. Hard Treatment 2. inflicted for a violation of legal rules 3. on the actual or supposed violator of the rules 4. imposed by people other than the violator 5. who have the authority to do so under the rules of the legal system. (H.L.A. Hart, 1968; Feinberg & Gross, 1995)

5
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).

6
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.

7
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.

8
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).

9
Emotion Resentment Indignation Hatred Judgment Disapproval Reprobation

10
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.

11
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.

12
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).

13
Criminal Trial (Insanity Defense) Conviction ∙ Guilt Determination ∙ Punishment ∙ Condemnation “NGRI” ∙ No Guilt Determination ∙ No Punishment ∙ No Condemnation

14
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.

15
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

16
Vignettes described assaults committed by defendants suffering from: Seizure disorder (low condemnation) PTSD/battered child syndrome Paranoid schizophrenia Antisocial personality disorder (high condemnation)

17
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.

18
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”

19
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

20
(Summary of main effects: Seizure < schiz = PTSD < Antisoc.)

21
Summary of main effect: assault < homicide

22
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.

23
Predicted Frequencies Observed Frequencies NGRIGuilty Percentage Correct NGRI66198.5 Guilty4233.3 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

24
Predicted Frequencies Observed Frequencies NGRIGuilty Percentage Correct NGRI66198.5 Guilty4233.3 Total Percentage Correct 93.2 Note: Constant-only model accuracy = 91.8%

25
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.148.0645.3321.0211.160 Attitudes.139.0703.9831.0461.149 Constant -24.08.458.0721.004- Cox & Snell R 2 =.234, Nagelkerke R 2 =.541

26
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.148.0645.3321.0211.160 Attitudes.139.0703.9831.0461.149 Constant -24.08.458.0721.004- Cox & Snell R 2 =.234, Nagelkerke R 2 =.541

27
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.148.0645.3321.0211.160 Attitudes.139.0703.9831.0461.149 Constant -24.08.458.0721.004- Cox & Snell R 2 =.234, Nagelkerke R 2 =.541

28
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.148.0645.3321.0211.160 Attitudes.139.0703.9831.0461.149 Constant -24.08.458.0721.004- Cox & Snell R 2 =.234, Nagelkerke R 2 =.541 A 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%.

29
Predicted Frequencies Observed Frequencies NGRIGuilty Percentage Correct NGRI102033.3 Guilty83481.0 Total Percentage Correct 61.1 Note: Constant-only model accuracy = 58.3%

30
Predicted Frequencies Observed Frequencies NGRIGuilty Percentage Correct NGRI102033.3 Guilty83481.0 Total Percentage Correct 61.1 Note: Constant-only model accuracy = 58.3%

31
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.034.0155.6041.0181.035 Attitudes.018.029.3891.5331.018 Constant -3.752.013.1521.076- Cox & Snell R 2 =.109, Nagelkerke R 2 =.147

32
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.034.0155.6041.0181.035 Attitudes.018.029.3891.5331.018 Constant -3.752.013.1521.076- Cox & Snell R 2 =.109, Nagelkerke R 2 =.147

33
Predicted Frequencies Observed Frequencies NGRIGuilty Percentage Correct NGRI50394.3 Guilty11945.0 Total Percentage Correct 80.8 Note: Constant-only model accuracy = 72.6%

34
Predicted Frequencies Observed Frequencies NGRIGuilty Percentage Correct NGRI50394.3 Guilty11945.0 Total Percentage Correct 80.8 Note: Constant-only model accuracy = 72.6%

35
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.058.0255.3451.0211.059 Attitudes.076.0393.7631.0521.079 Constant -10.93.4010.261.001- Cox & Snell R 2 =.191, Nagelkerke R 2 =.276

36
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.058.0255.3451.0211.059 Attitudes.076.0393.7631.0521.079 Constant -10.93.4010.261.001- Cox & Snell R 2 =.191, Nagelkerke R 2 =.276

37
97% of participants returned a verdict of “guilty.” Condemnation scores could not predict verdicts better than the constant-only model. (X 2 = 0.834, p =.361) But... When GBMI was a verdict option, 2 participants returned verdicts of NGRI, 13 of GBMI, and 61 of guilty.

38
Predicted Frequencies Observed Frequencies GBMIGuilty Percentage Correct GBMI1127.7 Guilty061100 Total Percentage Correct 83.8 Note: Constant-only model accuracy = 82.4%

39
Predicted Frequencies Observed Frequencies GBMIGuilty Percentage Correct GBMI1127.7 Guilty061100 Total Percentage Correct 83.8 Note: Constant-only model accuracy = 82.4%

40
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.053.0244.9951.0251.054 Attitudes.056.0421.7981.1801.058 Constant -7.624.033.5721.059- Cox & Snell R 2 =.106, Nagelkerke R 2 =.176

41
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.053.0244.9951.0251.054 Attitudes.056.0421.7981.1801.058 Constant -7.624.033.5721.059- Cox & Snell R 2 =.106, Nagelkerke R 2 =.176

42
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.

43
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.

44
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)

45
Charging Decision Criminal Court ∙ Guilt Determination ∙ Punishment ∙ Condemnation Juvenile Court ∙ No Guilt Determination* ∙ No Punishment* ∙ No Condemnation*

46
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

47
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

48
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

49
Predicted Frequencies Observed Frequencies JuvenileCriminal Percentage Correct Juvenile1231291.1 Criminal501927.5 Total Percentage Correct 69.6 Note: Constant-only model accuracy = 66.2%

50
Predicted Frequencies Observed Frequencies JuvenileCriminal Percentage Correct Juvenile1231291.1 Criminal501927.5 Total Percentage Correct 69.6 Note: Constant-only model accuracy = 66.2%

51
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.059.01319.621.0011.061 Constant -2.92.53929.451.001- Cox & Snell R 2 =.106, Nagelkerke R 2 =.147

52
Binary Logistic Regression Analysis—condemnation + attitudes Predictor ßS.E. ß Wald X 2 dfp e ß (odds ratio) Condemnation.059.01319.621.0011.061 Constant -2.92.53929.451.001- Cox & Snell R 2 =.106, Nagelkerke R 2 =.147

53
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.

54
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.

55
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

56
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.

Similar presentations

Presentation is loading. Please wait....

OK

INTRODUCTION TO THE LAW OF EVIDENCE

INTRODUCTION TO THE LAW OF EVIDENCE

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on icici prudential life insurance Ppt on strategic brand management by keller Ppt on patient monitoring system using gsm Ppt on basic etiquettes Ppt on smes Ppt on effect of global warming on weather we like it or not Free ppt on mobile number portability status Ppt on organic farming in india Ppt on pollution and its types in hindi Ppt on old age problems in india