Bibliographic record
Abstract
How ought we to evaluate and respond to expressions of anger and resentment? Can philosophical analysis of resentment as the emotional expression of a moral claim help us to distinguish which resentments ought to be taken seriously? Philosophers have tended to focus on what I call ‘reasonable’ resentments, presenting a technical, narrow account that limits resentment to the expression of recognizable moral claims. In the following paper, I defend three claims about the ethics and politics of resentment. First, if we care about socially just processes of reconciliation, we have good reason to pay attention to the logic of resentments. Second, the account philosophers offer of resentment – its distinctive features, aims, rationality, and gratification – will affect the conclusions we draw about which actual resentments to take seriously, which aspects of resentful claims need addressing, and what it means to address and repair them. In contesting definitions of resentment, I argue, we do more than simply perform housekeeping in philosophical taxonomies of emotion. Restricting our understanding to essentially ‘moral’ cases may cause us to lose sight of expressly political resentments. Instead, I argue, a plausible account of resentment must acknowledge that we resent violations and threats that are not necessarily self-pertaining, may not be expressible as individual, discrete injuries, and cannot always be construed as moral threats. Second, given the dependence of moral judgments on a broader horizon of moral possibility, philosophical standards of ‘reasonable’ or ‘appropriate’ resentment cannot avoid being politically charged. Thus, the widely accepted account of ‘reasonable’ resentment cannot make philosophical sense of the most interesting and perplexing cases. Ironically, a theoretical measure designed to revalue emotional expressions of moral protest may result in the exclusion and silencing of those with the most reasons to protest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".