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Record W2944468236 · doi:10.1017/err.2019.20

“Lock This Whore Up”: Legal Violence and Flows of Information Precipitating Personal Violence against People Criminalised for HIV-Related Crimes in Canada

2019· article· en· W2944468236 on OpenAlex

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

VenueEuropean Journal of Risk Regulation · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsPunishment (psychology)CriminologyLock (firearm)State (computer science)Information flowFace (sociological concept)Big dataFrame (networking)Convergence (economics)Political scienceComputer securityBusinessInternet privacySociologyPsychologySocial psychologyEngineeringComputer scienceEconomicsEconomic growthSocial science

Abstract

fetched live from OpenAlex

This article examines the convergence of myriad forms of information on people who come to be targets of state and public control due to the perceived risk they present through having been alleged to have not disclosed their HIV-positive status to sex partners. Attending to the material, violent impacts of criminalisation – violence, both legal and extralegal – this article outlines how punishment is enhanced and amplified through the flow of information. Focusing on the material impacts of flows of information about the daily lives of people who face criminalisation moves analysis beyond solely a theoretical object of inquiry and helps to frame an understand that the effects of big data operate not just “within” big data surveillance, but also “beyond” big data surveillance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.832

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.229
Teacher spread0.222 · 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