The Economic Case for Reform: Rethinking Juvenile Incarceration in the United States
Bibliographic record
Abstract
This paper presents a comprehensive economic analysis challenging the fiscal efficacy of punitive juvenile justice models in the United States (U.S.). While late twentieth century incarceration policies coincided with declining youth crime rates, this correlation obscures underlying structural determinants - macroeconomic expansion, immigration reform, and strengthened community networks - that drove crime reduction. Through integrating theoretical frameworks from economics, developmental neuroscience, and criminology, this study exposes the empirical shortcomings of deterrence-based models, particularly when applied to juvenile populations. The analysis reveals that punitive incarceration triggers cascading losses in human capital, including lower educational attainment, diminished lifetime earning potential, and the deepening of multigenerational poverty. In sharp contrast, rehabilitative interventions grounded in therapeutic education, psychological support, and structured reintegration consistently yield significantly superior cost-benefit outcomes across both public safety and macroeconomic dimensions. These empirical findings underscore the urgent need for systemic reform toward evidence-based models that prioritize human capital development, advancing both societal well-being and fiscal sustainability through targeted youth investment rather than punitive, custodial confinement.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| 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".