Bibliographic record
Abstract
When the epistemic chips are down, what ought we believe about our friends? The debate over epistemic partiality bounces between two standard answers: the classic view that we ought believe only for evidential or epistemic reasons, and the partialist view that sometimes we ought to believe better about our friends than the evidence permits. I carve a middle ground, arguing that what we owe our friends is not belief but rather acceptance. I deploy an account on which accepting involves suppressing belief’s characteristic role of guiding cognition, reasoning, and action—a profile I motivate via structural analogy to familiar mechanisms in emotion regulation. The acceptance view highlights novel features of the psychological landscape of partiality cases, capturing the thoroughgoing regulation of diverse cognitive mechanisms, and a diachronic profile of commitment, cognitive effort, and self-control. Moreover, I argue that acceptance avoids pressing objections to the standard views: worries about doxastic control and rationality in friendship (for strong partialist views) and insincerity and the psychological dimension of friendship (for simple evidentialist views). Beyond just intervening in the epistemic partiality debate, a central motivation is to show that this mechanistically precise, psychologically-focused account reveals that acceptance—often dismissed as an unsatisfying cousin to belief—has more resources to make progress on issues in the ethics of belief than is often appreciated.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.057 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".