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Record W2902971655

Ipeelee and the Duty to Resist

2018· article· en· W2902971655 on OpenAlexaffabout
Marie-Ève Sylvestre, Marie-Andrée Denis-Boileau

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIndigenousLegal pluralismDutyResistance (ecology)Political scienceLawHegemonyPluralism (philosophy)Interpretation (philosophy)State (computer science)Criminal justiceJudicial interpretationLaw and economicsSociologyLegal professionEpistemologyLegal realism
DOInot available

Abstract

fetched live from OpenAlex

In this article, the authors take a critical look at s. 718.2(e) of the Criminal Code and how courts have interpreted it after Ipeelee from a legal pluralism standpoint. They suggest that the interpretation given by the Court opens the way to a form of resistance from the judiciary against the problem of Indigenous over-representation in the criminal justice system and the hegemonic approach of the Canadian state with respect to Indigenous legal orders. However, based on a thorough analysis of 635 decisions rendered after Ipeelee by trial and appellate courts between 2012 and 2015, the authors conclude that this innovative approach was, in turn, met with significant resistance by judges. The authors finally address the main practical and epistemological hurdles that can explain the limited impact of that approach in sentencing and suggest that this resistance could be overcome by promoting judicial innovation as well as the revitalization of Indigenous legal systems.

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)0.014
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.040
Scholarly communication0.0120.008
Open science0.0020.014
Research integrity0.0090.017
Insufficient payload (model declined to judge)0.0070.001

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 head0.009
GPT teacher head0.296
Teacher spread0.287 · 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 designQualitative
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

Citations5
Published2018
Admission routes2
Has abstractyes

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Same venueSSRN Electronic JournalSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207