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Record W2771770643 · doi:10.18192/jpp.v26i1-2.2284

More Stormy Weather or Sunny Ways? A Forecast for Change by Prisoners of the Canadian Carceral State

2017· article· en· W2771770643 on OpenAlexvenueaboutno aff
Jarrod Shook, Bridget McInnis

Bibliographic record

VenueJournal of Prisoners on Prisons · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceState (computer science)Criminal justiceEconomic JusticeContext (archaeology)Power (physics)CriminologyIndigenousLawPublic administrationSociologyHistory

Abstract

fetched live from OpenAlex

Upon being elected, Prime Minister Justin Trudeau (2015) mandated the Minister of Justice and Attorney General of Canada Jody Wilson Raybould to review criminal justice laws, policies and practices enacted during the 2006-2015 period where successive Conservative federal governments were in power. Recognizing that the knowledge produced by prisoners, particularly when brought together with academic arguments, can serve to enlighten public discourse about the current state of carceral institutions, the Journal of Prisoners on Prisons undertook a Canada-wide consultation with federal prisoners with regard to what changes have occurred in the institutions where they have served time in the last decade. This paper, which summarizes that consultation, begins with an overview of the Conservative punishment agenda, followed with a thematic review of how these changes have affected prisoners and what they would like to see moving forward in the context of the governments promised review of the Canadian criminal justice system. Representing the captive from every region of the country at all security levels and privileging the voices of Women, Black, Indigenous, LGBTQ, and Elderly prisoners, the ten most prevalent areas of concern and reform that emerged include: sentencing, mental health, health care, food, prisoner pay, old age security, education and vocational training, case management and staff culture, parole and conditional release conditions, and pardons.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.356
Teacher spread0.272 · 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 teacher head, not a consensus.

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

Citations6
Published2017
Admission routes2
Has abstractyes

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