Reparative justice

Weitekamp, Elmar

doi:10.1007/bf02249525

Cited by 18 publications

(15 citation statements)

References 9 publications

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“…There are a broad range of alternative responses, reflecting the idea that both the transgressor and the victim can be differentially valued depending on one’s social preferences and conceptual sense of justice. For instance, some people may prefer to compensate the victim 11 , or punish the transgressor such that the penalty is proportionate to the harm committed 12 , preferences that may prove to play powerful roles in motivating the restoration of justice. Although existence of alternative forms of justice restoration date back as far as four millennia ago 13 , no research that we are aware of has examined these alternatives alongside the prototypical punitive options.…”

Section: Introductionmentioning

confidence: 99%

Fairness violations elicit greater punishment on behalf of another than for oneself

et al. 2014

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Classic psychology and economic studies argue that punishment is the standard response to violations of fairness norms. Typically, individuals are presented with the option to punish the transgressor or not. However, such a narrow choice set may fail to capture stronger alternative preferences for restoring justice. Here we show, in contrast to the majority of findings on social punishment, that other forms of justice restoration (e.g., compensation to the victim) are strongly preferred to punitive measures. Furthermore, these alternative preferences for restoring justice depend on the perspective of the deciding agent. When people are the recipient of an unfair offer, they prefer to compensate themselves without seeking retribution, even when punishment is free. Yet when people observe a fairness violation targeted at another, they change their decision to the most punitive option. Together these findings indicate that humans prefer alternative forms of justice restoration to punishment alone.

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Section: Introductionmentioning

confidence: 99%

Fairness violations elicit greater punishment on behalf of another than for oneself

et al. 2014

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“…Finally, we relate the overall training data to the different ground truth annotations using the epsilon-SVR regression strategy. In this case, when using the leave-one-out strategy, we obtain a prediction for each sample within the same range as the quantified annotations [1,5]. In this setting, we also ran the experiment twice: first, we considered all features, and then Weighted feature selection when using AdaBoost and SVM for the grouping response cases presenting the highest accuracy when predicting receptivity, agreement, and satisfaction.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…Some of the sessions also involved accompanying persons, either a professional from the specific center, or experts in some particular field relevant to the case under discussion. 1 The maximum number of people in the recorded sessions was five. Each recorded session 2 provided audio-RGB-depth information.…”

Section: Data Collectionmentioning

confidence: 99%

“…Learning is performed using leave-one-out validation, keeping one sample out of the testing each time. Since the total number of samples is small and the ground truth values are quantified within ranges [1,5] (as for the nervousness features), we simplified the problem by grouping the different response degrees into binary groups, but we also performed a posterior regression analysis. In the case of a binary setup, the value 3 can be considered as being either high or low.…”

Section: A Setting and Validation Measurementsmentioning

confidence: 99%

“…R ESTORATIVE justice is an international social movement for the reform of criminal justice. This approach to justice focuses on the needs of the victims, who take an active role in the process, while offenders are encouraged to take responsibility for their actions to repair the harm they have done [1]. It differs, therefore, with respect to the classic justice framework, where justice seeks to satisfy abstract legal principles or to directly punish offenders.…”

Section: Introductionmentioning

confidence: 99%

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Non-verbal communication analysis in Victim–Offender Mediations

Ponce-López

Escalera

Pérez

et al. 2015

Pattern Recognition Letters

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Abstract-In this paper we present a non-invasive ambient intelligence framework for the semi-automatic analysis of nonverbal communication applied to the restorative justice field. In particular, we propose the use of computer vision and social signal processing technologies in real scenarios of Victim-Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real world Victim-Offender Mediation sessions in Catalonia in collaboration with the regional government. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state-of-the-art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1][2][3][4][5] for the computed social signals.

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