Double Jeopardy Hypothesis and Hiring Bias Against Indigenous Men with Criminal Records
Bibliographic record
Abstract
Indigenous men with criminal records experience compounded exclusion in the labor market due to intersecting stigmas of race and criminal history, a phenomenon explained by the Double Jeopardy Hypothesis. This paper explores these dynamics through three experimental studies examining hiring biases against Indigenous applicants compared to their Black and White counterparts. Results reveal that Indigenous men with criminal records are disproportionately under selected for employment opportunities, even in comparison to Black applicants with similar histories. Techniques like name anonymization, revised language describing criminal records, and ban-the-box policies were found to be ineffective for Indigenous applicants, contrasting with their success for other groups. These findings highlight how compounded disadvantages uniquely exclude Indigenous men with criminal records from equitable labor market access, reinforcing systemic barriers. This research contributes to the limited empirical literature on Indigenous peoples with criminal records and employment and underscores the need for tailored, equity-focused interventions informed by the Double Jeopardy Hypothesis.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".