Breaking Barriers: Navigating Stigma, Identity, and Opportunity for Justice- Involved Workers
Bibliographic record
Abstract
Justice-involved individuals, those with criminal records, face significant and persistent barriers to employment, often exacerbated by stigma and structural inequities. Despite organizational scholars’ growing interest in understanding labor market inequalities, research has largely focused on race and gender, leaving the unique challenges of this population underexplored. This symposium brings attention to the systemic barriers encountered by justice-involved individuals while examining how stigma interacts with organizational structures, hiring practices, and individual strategies for reintegration. The presentations in this symposium cover diverse dimensions of these challenges. One study investigates how justice-involved individuals navigate the complexities of digital-era job searches, where their criminal records are easily accessible online. Another explores how justice-involved entrepreneurs strategically frame their criminal records in narratives to mitigate stigma and build trust with investors. A third presentation examines hiring managers’ contradictions between their stated support for second- chance hiring and their exclusionary practices, highlighting organizational barriers to equity. The fourth focuses on how incarcerated women reconstruct their professional identities within prison-based work environments, leveraging organizational respect and mentorship to foster resilience and empowerment. Finally, the symposium concludes with a study on how gender moderates the stigma of criminal records in hiring evaluations, revealing compounded penalties for justice-involved women. This symposium seeks to advance theoretical understanding of labor market inequality by centering justice-involved individuals, a historically marginalized population, in organizational research. By offering actionable insights into individual strategies and organizational practices, the symposium provides a foundation for creating more inclusive and equitable labor markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".