Bibliographic record
Abstract
The scientific study of wrongful convictions has been ongoing for the past few decades. These studies have worked to quantify wrongful convictions, identify contributing factors, and understand the negative implications to society and the individuals who experiences a wrongful incarceration. The majority of existing studies focus on what leads to a wrongful conviction, with fewer studies examining the community reentry processes of wrongfully convicted individuals. Those studies that do specifically focus on after-release experiences among wrongfully convicted individuals generally focus on the wide range of experiences that wrongfully convicted individuals have in terms of community reentry. The current study aims to contribute to these existing conversations on post-release experiences of wrongfully convicted individuals by focusing on a very specific aspect of community reentry, employment. Utilizing qualitative interviews with innocence organizational employees, individuals who work closely with wrongfully convicted individuals before their release and often maintain relationships after their release as well, this study examines how wrongful convictions impact employment. Findings show that obtaining innocence in often a long and complex process, resulting in numerous barriers that individuals must navigate in the job market. Organizational employees discuss the many barriers that their clients often encounter and the ways in which they, their organization, and wider society can assist wrongfully convicted individuals in the community reentry efforts more broadly. Policy implications are also discussed to aid wrongfully convicted individuals after their release.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".