Custodial Parole Sanctions and Earnings after Release from Prison
Bibliographic record
Abstract
Although the labor market consequences of incarceration in prison have been central to the literature on mass incarceration, punishment, and inequality, other components of the growing criminal justice system have received less attention from sociologists. In particular, the rise of mass incarceration was accompanied by an even larger increase in community supervision. In this paper, we examine the labor market effects of one frequently experienced aspect of post-prison parole, short-term custody for parole violations. Although such sanctions are viewed as an alternative to returning parole violators to prison, they have the potential to affect labor market outcomes in ways similar to imprisonment, including both adverse and positive effects on earnings. We estimate that parolees lost approximately 37 percent of their earnings in quarters during which they were in short-term custody. Although their earnings tended to increase in the quarter immediately following short-term custody-consistent with the stated intentions of such sanctions-parolees experienced further earnings loss over the longer term after such sanctions. In the third quarter following a short-term custody sanction, earnings are lowered by about 13 percent. These associations are larger for those who were employed in the formal labor market before their initial incarceration.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".