Crime and Discrimination in the Labor Market: An Empirical Approach
Bibliographic record
Abstract
This paper investigates the existence of wage discrimination to inmates. Based on data collected from the Coordination Center for the Execution of Penalties and Alternative Measures (CEPEMA) for people serving in an open prison in Brasília (DF), a comparative approach was conducted with data collected from PNAD. It was then possible to verify using the decomposition process of Oaxaca-Ransom that there is statistical discrimination regarding to ex-convicts in the job market. Furthermore, it has been noticed that the full labor market participation of prisoners seems to be compromised to the extent that the empiricalresults support the assumption of Nagin and Waldfogel (1993). It indicates that access of the individual who been in prison to the job market is limited to the so-called spot market or temporary labor market. This segment of the labor market should not be confused with the so-called part time.Thus, one of the negative effects that can be understood from this is a reduction in the current value of the individual's discounted income, since long-term jobs are those that offer higher income perspectives.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".