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Record W7118172979 · doi:10.1177/26326663251350257

The re-centralization of work in prison. The Punta de Rieles experiment in Montevideo, Uruguay

2025· article· en· W7118172979 on OpenAlexafffund
Fernando Ramón Avila

Bibliographic record

VenueIncarceration · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrisonAutonomyWork (physics)Corporate governanceEmpowermentState (computer science)

Abstract

fetched live from OpenAlex

This paper examines the approach to prisoner labour and work programs at Punta de Rieles prison in Montevideo, Uruguay-a medium-security, non-traditional prison that offers a lens to interrogate the intersections of labour and punishment. At Punta de Rieles, prisoners are responsibilized as part of a broader governance strategy, where the state delegates significant autonomy to prisoners to engage in activities deemed 'productive' within a framework of "governing at a distance." This strategy has formalized a prison-based labour market characterized by horizontal labour relations, expanded opportunities, prisoner participation in the regulation of work, and state oversight of labour relations and business initiatives. While this model reframes and recenters work in prisons, shifting its focus from direct disciplinary control to self-governance and economic integration, it also raises critical questions about how labour functions as a tool of both autonomy and discipline. By embedding labour within formalized economic structures and regulation by prison authorities, the Punta de Rieles model complicates traditional understandings of prison work, revealing the blurred boundaries between empowerment and control, autonomy and exploitation. This analysis underscores the need for a nuanced critique of prison labour as a site where economic, social, and penal logics converge.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.340
Teacher spread0.322 · 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 teacher head, 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

Citations0
Published2025
Admission routes2
Has abstractyes

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