Neoliberalism, mass incarceration, and the US debt–criminal justice complex
Bibliographic record
Abstract
While debtors’ prisons in the United States were outlawed in the early 19th century, recent reports indicate that a growing number of people across the US are currently imprisoned for debt. This process typically occurs in two ways: debtors are found in contempt of court for non-appearance after being pressured into repaying consumer debt, or offenders are incarcerated for unpaid legal financial obligations (LFOs) incurred in the criminal justice system. While numerous legal scholars have examined these practices, little scholarship has situated this phenomenon within the politico-economic landscape of neoliberalism. Seeking to chart the intersections between economic restructuring and the expansion of the carceral state over the past 40 years, this article situates the modern debt–criminal justice complex within the broader historical trajectories of debt, incarceration, and institutional racism within the US. Emphasizing the centrality of US state reforms implemented under neoliberalism, this article examines the transformation of the federal welfare system toward ‘workfare’, as well as bankruptcy reforms implemented in the context of rising consumer debt during the 1990s and early 2000s. I maintain that these overlapping transformations, alongside the expansion of the criminal justice apparatus, were central historical processes that shaped the modern debt–criminal justice complex in the US, which continues to criminalize low-income and racialized populations across the country.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".