Bibliographic record
Abstract
In this article, I defend a conception of bitterness as a moral emotion and offer an evaluative framework for assessing when instances of bitterness are morally justified. I argue that bitterness is a form of unresolved anger involving a loss of hope that an injustice or other moral wrong will be sufficiently acknowledged and addressed. Orienting the discussion around instances of bitterness in response to social and political injustices, I argue that bitterness is sometimes morally justified even if it is ultimately undesirable to bear. I then suggest that focusing only on the harms and risks of bitterness can distract from its positive role as a moral reminder about a past or persistent injustice, indicating that there is still moral and often political work left to do. Finally, I address the concern that bearing bitterness may lead to despair and inaction. I respond by arguing that moral agents can and do persist in their moral and political struggles with bitterness, and without hope that their efforts will be successful.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".