Exploring the overlooked: women, work and criminal history
Bibliographic record
Abstract
Purpose This paper aims to explore how incarcerated women prepare to manage the stigma of a criminal history as they look to re-enter the workforce after release from incarceration. Design/methodology/approach This paper uses a qualitative, case study research design including interviews and observations to explore the experiences and self-perceptions of incarcerated women within the context of employment. Findings Five themes that emerged and influenced the perception of stigma as these incarcerated women prepared for release into the labor market were career self-efficacy, the intersection of identity (women and criminal history), self-perceptions of prison identity, stigma disclosure and social support for employment. Research limitations/implications As the management literature expands to include more diverse and marginalized populations, current understanding of theories and concepts, such as multiple identities and stigma disclosure, may operate differently as compared to traditional management samples. Practical implications Organizations can collaborate with correctional facilities to ensure that individuals with a criminal history are trained and prepared to re-enter the workplace upon release. Social implications As employment is one of the biggest determinants of recidivism (i.e. return to incarceration) for individuals with a criminal history, organizations have the unique ability to assist in substantially decreasing the incarcerated population. Originality/value This study explores criminal history and highlights some of the nuances to consider when exploring an understudied and marginalized population, such as women with a criminal history.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".