The re-centralization of work in prison. The Punta de Rieles experiment in Montevideo, Uruguay
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".