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Record W4404290456 · doi:10.1177/26326663241286171

Cultures of transparency in carceral governance: Lessons from the global North/South divide

2024· article· en· W4404290456 on OpenAlexafffundabout
Hollis Moore, José A. Brandariz, Vicki Chartrand, Jennifer M. Kilty, Dawn Moore, Máximo Sozzo, Sarah Turnbull

Bibliographic record

VenueIncarceration · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of OttawaBishop's UniversityUniversity of WaterlooCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTransparency (behavior)Corporate governancePolitical scienceBusinessLawFinance

Abstract

fetched live from OpenAlex

This conceptual article offers new ways to map and understand the role of transparency in carceral governance. Mobilizing our expertise in our respective fields of study, we comparatively reflect on case studies of carceral transparency in Argentina, Canada, and Spain. In each, we decentre forms of transparency favoured by carceral authorities by considering the range of mechanisms and actors at play in the production of transparency. Taken together, our accounts of cultures of transparency in prisons and migrant detention facilities in the global north and south highlight absences and presences of different means of generating transparency across carceral sites and denaturalize northern and state-centric ideas about carceral transparency. Ultimately, our juxtaposition of three cultures of transparency reveals the range of means of generating carceral knowledge and the potential scope for its dissemination. Amidst persistent human rights violations, this work underlines the need for further southernized research on transparency to shape possibilities of carceral governance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.347
Teacher spread0.318 · 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
Published2024
Admission routes3
Has abstractyes

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