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Record W2018282464 · doi:10.1097/jfn.0b013e31827a1f66

Total Control

2013· article· en· W2018282464 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.

Bibliographic record

VenueJournal of Forensic Nursing · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsControl (management)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The aim of this paper is to explore the relationship between mandatory HIV testing and the institutional management of inmates in U.S. prisons. Mandatory HIV testing has been largely overlooked by the nursing community even though it has important human rights and ethical implications. Drawing on the work of Goffman (1990) on the inner workings of total institutions, the present article critically examines the deployment of mandatory HIV testing in U.S. prisons. To set the stage, we define mandatory HIV testing and describe the methods of HIV testing currently used in U.S. prison settings. Then, we provide a brief overview of the concept of total institution and the mortification process. Finally, we expand on the relationship between mandatory HIV testing and much larger institutional objectives of total control, total structuring, total isolation, and separation of inmates from society (as summarized by Farrington, 1992). And lastly, we provide a brief discussion on the implications of mandatory HIV testing (as a method of HIV testing) from a nursing perspective.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.360

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 head0.013
GPT teacher head0.301
Teacher spread0.288 · 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