The long struggle: An agonistic perspective on penal development
Bibliographic record
Abstract
Bringing together insights from macro-level theory about “mass imprisonment” and micro-level case studies of contemporary punishment, this article presents a mid-level agonistic perspective on penal change in the USA. Using the case of the “rise and fall” of the rehabilitative ideal in California, we spotlight struggle as a central mechanism that intensifies the variegated (and sometimes contradictory) nature of punishment and drives penal development. The agonistic perspective posits that penal development is fueled by ongoing, low-level struggle among actors with varying amounts and types of resources. Like plate tectonics, friction among those with a stake in punishment periodically escalates to seismic events and long-term shifts in penal orientations, pushing one perspective or another to the fore over time. These conflicts do not occur in a vacuum; rather, large-scale trends in the economy, politics, social sentiments, inter-group relations, demographics, and crime affect—but do not fully determine—struggles over punishment and penal outcomes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".