Carceral Chicago: Making the Ex‐offender Employability Crisis
Bibliographic record
Abstract
Abstract This article explores the urban labor market consequences of large‐scale incarceration, a policy with massively detrimental implications for communities of color. Case study evidence from Chicago suggests that the prison system has come to assume the role of a significant (urban) labor market institution, the regulatory outcomes of which are revealed in the social production of systemic unemployability across a criminalized class of African–American males, the hypertrophied economic and social decline of those ‘receiving communities’ to which thousands of ex‐convicts return, and the remorseless rise of recidivism rates. Notwithstanding the significant social costs, the churning of the prison population through the lower reaches of the labor market is associated with the further degradation of contingent and informal‐economy jobs, the hardening of patterns of radical segregation, and the long‐term erosion of employment prospects within the growing ex‐offender population, for whom social stigma, institutional marginalization and economic disenfranchisement assume the status of an extended form of incarceration. Résumé La politique publique d’incarcération massive, aux implications largement préjudiciables aux communautés de couleur, affecte également le marché du travail des villes. Une étude de cas sur Chicago indique que le système pénitentiaire a fini par devenir une institution importante du marché du travail (urbain) dont les réglementations se traduisent à la fois par la production sociale d’une inemployabilité systémique pour une classe criminalisée de males afro‐américains, par le déclin économique et social hypertrophié des ‘communautés d’accueil’ vers lesquelles retournent des milliers d’ex‐prisonniers, et par l’accroissement impitoyable des taux de récidive. Malgré de forts coûts sociaux, le brassage de la population carcérale dans les niveaux inférieurs du marché du travail se combine à la dégradation accrue des postes occasionnels et offerts par l’économie parallèle, mais aussi au durcissement des types de ségrégation radicale et à une érosion durable des perspectives d’emploi au sein de la population grandissante des ex‐délinquants pour lesquels stigmatisation sociale, marginalisation institutionnelle et non‐reconnaissance économique revêtent une forme d’incarcération prolongée.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".