Public Perceptions of the Stigmatization of Wrongly Convicted Individuals: Findings from Semi-Structured Interviews
Bibliographic record
Abstract
Many exonerees report stigmatizing experiences and difficulties securing gainful employment post-incarceration. Although researchers have begun to investigate public perceptions of wrongful conviction, there remains a dearth of knowledge about public perceptions of exonerees. To provide insight into how the public perceives exonerees, face-to-face interviews were conducted with members (n=30) of a suburban city in South Central Ontario. Data analysis included a constructed grounded approach to reveal emergent themes in the transcripts. All interviewees acknowledged that wrongly convicted individuals are stigmatized by the public and that this can have negative effects in many of their lived experiences. In addition, findings of this exploratory study suggest that some interviewees, indirectly or directly, stigmatize exonerees in their responses while being interviewed—lending insight into how the public views and reacts to exonerees. Findings and policy implications are theoretically framed in Erving Goffman’s (1963) seminal work on stigma. Implications include the potential role of research and education in informing community members, and all levels of government, about wrongful convictions in general, and the negative implications of stigma, in particular.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".