FROM PRISON TO PRODUCTIVITY: WHY YOU SHOULD HIRE FORMERLY INCARCERATED PEOPLE
Bibliographic record
Abstract
Abstract The author, who leads Transformation Solutions for Mercer US and Canada, discusses a timely and potentially controversial topic: hiring formerly incarcerated people, a group that has a disproportionately high unemployment rate. However, she points out that structural elements in the U.S. economy mean that organizations will continue to search for qualified workers: “Labor force participation remains below pre‐pandemic levels; the labor force is expected to grow at a far slower rate than in previous decades.” She points to hiring research and reporting at Johns Hopkins Medicine and the Illinois Prison Project, and claims that formerly incarcerated people are not risky hires, and that they are more loyal employees with unique talents. However, it is not that simple: “hiring and retaining this group is far more complicated than simply focusing on them.” She offers hiring strategies such as, “To open up your organization to hire more formerly incarcerated people, you need to also figure out the hidden blockers – what do you assume is needed for roles on your team that might not be needed (but doesn’t formally appear on the job description)?” In addition, “you have to ensure that the very mechanics of the process of getting hired don’t exclude them (even if they technically qualify for the role).”
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".