Methods of Calculating the Marginal Cost of Incarceration: A Scoping Review
Bibliographic record
Abstract
Criminal justice reforms and corrections cost forecasts require appropriate estimates of the marginal costs of incarceration to adequately assess cost savings and projections. Average costs are simple to calculate while marginal cost calculations require much more detailed data and advanced methods. We undertook a scoping review to identify, report, and summarize the existing academic and gray literature covering the different estimation methods of calculating the marginal costs of incarceration, following the Arksey and O’Malley framework. Eighteen publications met criteria for inclusion in this review, with only one from the peer-reviewed literature. The three main approaches in the literature and their use are reviewed and illustrated. We conclude that there is a lack of, and need for, peer-reviewed literature on methods for calculating the marginal cost of incarceration, and marginal cost estimates of incarceration, to assist program evaluation, policy, and cost forecasting in the field of corrections.
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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 | 0.044 | 0.187 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".