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Record W3121874359

Infusing Reconciliation into the Sentencing Process

2019· article· en· W3121874359 on OpenAlexaffabout
Colton Fehr

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsImprisonmentCriminal justiceSentenceParliamentPolitical scienceDialogical selfState (computer science)LawOrder (exchange)CriminologyEconomic JusticeSentencing guidelinesSociologyPsychologySocial psychologyBusinessPoliticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

The Canadian criminal justice system has long been criticized for its over-incarceration of First Nations peoples. In response, Parliament required that courts consider the unique circumstances impacting First Nations persons before passing sentence, and in particular before imposing a sentence of imprisonment. Although these efforts are important for reconciling relations between First Nations people and Canada, scholars have paid inadequate attention to whether the process in which the vast majority of sentencing hearings are conducted might also hinder reconciliation. In this article, I contend that the traditional order of sentencing submissions will generally fail to facilitate important dialogue between state representatives and First Nations people. I propose that reversing the order in which counsel make sentencing submissions would allow for a dialogical approach to sentencing that would better ensure that First Nations offenders feel they are treated fairly by the criminal justice system.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.115
metaresearch head score (Gemma)0.246
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.607

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1150.246
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0130.013
Scholarly communication0.0140.016
Open science0.0040.017
Research integrity0.0100.014
Insufficient payload (model declined to judge)0.0050.002

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 head0.010
GPT teacher head0.295
Teacher spread0.285 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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