Bibliographic record
Abstract
Revenge is a powerful word. It can conjure up the scheming, embittered individual, plotting the downfall of his enemies well beyond reason and morality – or, more seriously, tragic cycles of violence and blood vendettas, spiraling into entrenched civil conflict over generations. Philosophers have argued that the consequences and the moral psychology of revenge mean it is incompatible – even antithetical – to any plausible conception of moral repair. In this paper I challenge that incompatibility by suggesting that, in contexts of unresponsive and imperfect institutional justice, appropriate acts of vengeance may both create accountability and express solidarity, thus contributing to moral repair. Drawing on past work by French (2001) and MacLachlan (2016), as well as two films with feminist avengers as protagonists, Hard Candy (2005) and Promising Young Woman (2021), I sketch an approach to virtuous third-party vengeance as a starting point for broader, more reparative, understandings of legitimate personal interventions after wrongdoing.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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 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".