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Record W3022522540 · doi:10.1525/nclr.2015.18.4.510

Restorative Justice

2015· article· en· W3022522540 on OpenAlex
Alana Saulnier, Diane Sivasubramaniam

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNew Criminal Law Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsQueen's University
Fundersnot available
KeywordsRestorative justiceRetributive justiceEconomic JusticeWork (physics)PopularityPsychologyEngineering ethicsSociologyPolitical scienceCriminologyEngineeringSocial psychologyLaw

Abstract

fetched live from OpenAlex

As the popularity of restorative procedures increases, it is important to reflect on what we do and do not know about restorative justice, in order to enhance the effectiveness of restorative practices. In particular, we know little about the mechanisms that encourage success in restorative procedures. This article reviews research examining how, why, and for whom restorative procedures work. We consider how restorative processes differ from more traditional forms of retributive justice, and review the empirical research on factors driving people's perceptions of and responses to restorative justice. Through this overview of the existing knowledge base regarding why and for whom restorative procedures work, we draw attention to gaps in the restorative justice literature. We highlight the need for more focused research in understudied areas—in particular, we discuss the need for further development of experimental methods in restorative justice research—which will enable restorative justice scholars to develop more effective procedures that complement existing legal processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.162
GPT teacher head0.409
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it