‘Longtermer blues’: Penal politics, reform, and carceral experiences at Angola
Bibliographic record
Abstract
While much has been written about how and why punishment has transformed in recent years, less is known about how this supposed paradigm shift might have shaped the lives of prisoners. Through a content analysis of The Angolite prison news magazine from the years 1979 to 2001, this article traces Angola's trajectory over the last several decades of the 20th century, examines how this path was informed by national law-and-order trends, penal politics in Louisiana, and Angola's own unique history, and analyzes how this influenced carceral experiences. I find that there was no sharp transition from rehabilitation to managerialism at Angola. Instead, the focus in the 1970s was on bringing order and security to the ‘bloodiest prison in the nation’. Moreover, rather than solely a product of neo-liberalism, responsibilization rhetoric at Angola dates back to the 1950s, when inmate self-help organizations were first created. Consequently, this article encourages us to re-think the welfare/risk dichotomy and contributes to the growing body of literature that stresses the need to investigate changes in penality on a subnational scale. Significantly, this project also advances our understanding of late-modern US punishment by highlighting the frustration, despair, and struggle with hopelessness experienced by Angola's longtermers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".