What's in a Name? The Impact of Labels on Attitudes Toward Exonerees
Bibliographic record
Abstract
As exonerations have increased, so too has research into exonerees’ post-release challenges, including stigma, discrimination, mental illness, and inadequate support. In so doing, researchers and advocates have described this population in varied ways, which may elicit differing attitudes. To explore that possibility, 188 citizens read a tweet in which we varied the label ascribed to a newly released prisoner (i.e., wrongly convicted, exonerated, innocent, or control), then reported their attitudes. Contrary to expectation, different labels did not produce different judgements of the individual’s character, criminality, or deservingness of support, which were consistently significantly more favourable for exonerees (regardless of label) than other formerly incarcerated people (control). Troubling, however, was that the terms wrongly convicted, exonerated, and innocent still led to some concerns that the individual was somehow involved in—or had committed—the crime for which he was erroneously convicted, that he may have committed other crimes in the past, and that he might commit crimes in the future. Implications are discussed in terms of stigma theory, growing media attention to wrongful convictions, and the disconnect between public and government support for post-release services.
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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 | 0.001 | 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 | 0.000 | 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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".