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Record W1562544773 · doi:10.4000/champpenal.9158

Landscapes of Violence

2015· article· en· W1562544773 on OpenAlex
Vicki Chartrand

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueChamp pénal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsBishop's University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Drawing on findings from prison inquiries and commissions, correctional policies and practices, news media accounts and prisoner testimonies, this article reconsiders the prevalence of violence against women in Canadian prisons. By adopting a broader conceptualization of violence, particularly as enacted and legitimated by the state, the article reveals how the prison setting generates a landscape of violence through the languages of gender and security, along with routine practices that come together to facilitate and heighten the conditions for violence to occur in seemingly, normal, benign, and necessary ways. This can include high risk designations, higher classifications, involuntary and forced transfers, deportation, strip-searches, administrative segregation, suicide watches, dry cells, transfers to men’s prisons, lack of medical attention, and the denial of support or service – all of which are often experienced in violent ways and can culminate into self-harm, suicide, and death. The more violent aspects of the prison system are made less visible through routine practices and a normal politics that minimize, obscure, or ignore violence. Given its inherently violent character, we have to question the use, necessity, and relevance of incarceration to enact justice.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.169

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 science0.0000.000
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 head0.061
GPT teacher head0.318
Teacher spread0.257 · 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