Neoliberalism and the erosion of the American criminal (in)justice system : a COVID perspective
Bibliographic record
Abstract
This paper examines the corrosive effects of neoliberalism on the U.S. criminal-justice system in the context of the current COVID-19 pandemic. I argue that neoliberalism, specifically a neoliberal governing rationality, has intensified the harms caused by the criminal-justice system-- harms that are rooted in the histories and legacies of slavery and racial injustice in the United Sates. The pandemic has exacerbated these harms in two ways. First, it has exposed the contradictions inherent in the classifications of labor, specifically in the realm of penal labor, and the impact that these categorizations have on the material conditions of workers and the value of labor. Next, I show how a neoliberal governing rationality has also strengthened the power and reach of the carceral state by reinforcing the myth of public safety that is predicated on tough-on-crime policies. Finally, I argue that the only strategy to confront the harmful effects of neoliberalism is through adopting radical frameworks for change, such as abolition.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".