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Record W7061050323

Parole in Canada: gender and diversity in the federal system

2016· book· en· W7061050323 on OpenAlexaboutno aff

Bibliographic record

VenueBIROn (Birkbeck, University of London) · 2016
Typebook
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)Power (physics)MulticulturalismIdeal (ethics)Prison populationPopulationCriminal justicePrisonGovernment (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Book snopsis: Just as Canada’s population has changed in the past four decades, so too has its prison population. The increasing diversity among prisoners raises important questions about how we punish those who break the law. Parole in Canada is the first book to explore how concerns about aboriginality, gender, and the multicultural ideal of “diversity” have been interpreted and used to alter federal parole policy and practice.
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\nUsing the Parole of Board of Canada as a case study, this book shows how certain facets of offender differences are selectively included for “accommodation,” while fundamental institutional structures, practices, and power arrangements remain unchanged. Sarah Turnbull argues that, as the current approach fails to challenge outdated notions about gender, race, and Aboriginality within the penal system, instead of addressing concerns around diversity, these measures end up contributing to further exclusion and discrimination within the system. By tracing the organizational approaches to gender and diversity in Canada’s federal parole system, this important book advances our understanding of penal change and highlights the challenges and complexities of accommodating offender diversities in the pursuit of a more “fair” and “appropriate” penality.
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\nScholars and upper-level students in the fields of law, criminology, sociology, gender studies, and Aboriginal studies will appreciate this analysis of diversity initiatives in parole policy and practice.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.598
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.201
Teacher spread0.180 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2016
Admission routes1
Has abstractyes

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