The overlooked workforce: Harnessing the talent of people with criminal histories
Bibliographic record
Abstract
Amid growing labor shortages and increased attention to workplace equity and inclusion, organizations are seeking new strategies to expand the available talent pool. One often-overlooked source of skilled and ready workers is individuals with a criminal record. Despite their potential, this group continues to face persistent barriers to employment, including stigma, ambiguous perceptions of risk, and the absence of structured hiring practices. While employer interest in fair-chance hiring is on the rise, actual outcomes remain limited. This article turns the focus toward employers themselves, drawing on nearly a decade of research and empirical analysis to explore the disconnect between stated openness and hiring behavior. We find that informal decision-making, reputational concerns, and subjective assessments of character and “fit” often override qualifications, perpetuating exclusion. To address this, we highlight the promise of a skills-based approach to hiring, one that prioritizes competencies over background. We outline actionable strategies for equitable hiring that include: formalizing fair-chance policies, training for bias awareness, and instituting transparent, criteria-based hiring processes. By reimagining risk and redefining merit, organizations can access untapped talent and take concrete steps toward more inclusive and effective workforce practices. • Employers support fair-chance hiring in principle, but hesitate to offer meaningful opportunities in practice. • We identify four key themes shaping employer decisions: Criminality, Employability, Work and Occupation, and Safety. • Exclusion is reinforced through subjective assessments and informal hiring practices. • We offer evidence-based, skills-focused strategies to help organizations implement fair chance hiring.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".