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Vagrancy as a Penal Problem: The Logistics of Administering Punishment in Late‐Nineteenth‐Century Canada

2012· article· en· W1602058307 on OpenAlexaffabout
Prashan Ranasinghe

Bibliographic record

VenueJournal of Historical Sociology · 2012
Typearticle
Languageen
FieldMedicine
TopicHistorical and Scientific Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsVagrancyPunishment (psychology)CriminologyLegislationCriminal justiceSociologyLawPolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract There exists a voluminous literature on the history of vagrancy and vagrancy legislation. However, virtually all of its focus has been on the manifestations of vagrancy as a social problem. What has not received attention is another important aspect to this history, one that finds its roots and geneses directly out of its construction as a social problem. This is the problem associated with the logistics surrounding the administration of punishment within correctional institutions, what I call vagrancy as a penal problem. The day‐to‐day operations of the criminal justice system, especially that part which was responsible for the administration of punishment, were immensely burdened because of the sheer number of vagrants who came through the system. This created logistical problems with respect to housing, classifying, maintaining and regulating prisoners, and especially with respect to vagrants, putting them to hard labour. This paper seeks to elucidate the penal problem of vagrancy by narrating the voices of the personnel who worked in correctional institutions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0310.024
Scholarly communication0.0070.001
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.272
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations4
Published2012
Admission routes2
Has abstractyes

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