HERO <i>AND</i> INMATE: WORK, PRISONS, AND PUNISHMENT IN CALIFORNIA'S FIRE CAMPS
Bibliographic record
Abstract
The debate over penal labor has been a vociferous one, as advocates and proponents have, for at least a century, made their case for or against the practice of putting prisoners to work. Nonetheless, conspicuously absent from this discussion have been the very people who most directly and intimately experience that work: prison inmates. In this article, I examine penal labor as it unfolds in one particular carceral site, namely California's prison fire camps in which state prisoners do manual labor and fight fires. The results reveal that binaries in which prison labor is positioned as either entirely good or entirely exploitative do not mesh well with the multifaceted experiences of those on the ground, who find elements of both at play. The fire camps are, therefore, an atypical case study that will hopefully serve as impetus for more research into the experiences of prisoner workers, as well as a starting point for deepening and complicating the existing debate over work and punishment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".