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Record W4327944375 · doi:10.1111/hojo.12503

The palimpsest of outdoor penal labour in California, 1915–2000

2023· article· en· W4327944375 on OpenAlexaff
Philip Goodman, Kaitlyn Quinn

Bibliographic record

VenueThe Howard Journal of Crime and Justice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsPhilips (Canada)University of Toronto
FundersNational Science Foundation
KeywordsPalimpsestAgency (philosophy)PrisonField (mathematics)SociologyState (computer science)CriminologyLawLaw and economicsPolitical scienceHistoryArchaeologySocial scienceComputer science

Abstract

fetched live from OpenAlex

Abstract In this article we examine the curious stability of outdoor penal labour in California in the 20th century against a shifting social and penal field. Analysing state archival data on prison highway and forestry camps between 1915 and 2000, we frame the persistence of these practices as evidence of a penal labour palimpsest. We demonstrate how the agency and interpretive innovation of penal administrators – as the architects and interpreters of this palimpsest – served as a stabilising mechanism akin to, but distinct from, existing theories of path dependence. Zooming out from the intricacies of the historical record, we position this case as revealing some of the limits of strict theories of path dependence and, instead, as offering a more dynamic understanding of the complex, intersecting and malleable ways in which history matters.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.477
Threshold uncertainty score0.949

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.328
Teacher spread0.299 · 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 designQualitative
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

Citations6
Published2023
Admission routes1
Has abstractyes

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