Showing Remorse: Reflections on the Gap between Expression and Attribution in Cases of Wrongful Conviction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper seeks first to show that persons who are convicted of crimes can be perceived as either remorseful or as lacking in remorse. This division establishes a moral hierarchy that has profound implications for the characterization and disposition of persons who are so designated. Second, using both Canadian and American cases, it looks at how inclusion in the category of the unremorseful affects the characterization and disposition of those who have been wrongfully convicted. Finally, it suggests that remorse is a major site of conflict between persons who are wrongfully convicted and officials within the criminal justice system, conflict that involves the use of institutional pressure to encourage the expression of remorse, on the one hand, and the mobilization of individual resources to resist those expressions, on the other.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it