Justifying leniency at a time of punitiveness: Federal clemency narratives in the United States
Bibliographic record
Abstract
Scholarship on contemporary US penality has paid little attention to practices opposing the punitive trend. This study explores clemency – official acts moderating punishment and its lasting consequences – as an executive back-end mechanism of leniency. To explore how clemency is discussed at a time of increasingly punitive penal policies, I conducted a qualitative analysis of 36 years’ worth of presidential statements on clemency from Reagan to Obama. This study revealed that three central justifications are used to validate clemency decisions: individuals’ deservingness, community benefits and justice ideals. Discussions of clemency challenge punitiveness by closing the social distance between individuals with criminal histories and law-abiding society and calling for moderation in punishment and penal reform. However, by using a justificatory tone and mirroring penal rationales, clemency statements are limited in inviting progressive change and at times actively drive and reinforce dominant punitive narratives.
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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 | 0.002 |
| 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) | 0.000 | 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 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".