Bibliographic record
Abstract
Abstract Prejudiced beliefs may certainly seem like defective beliefs. But in what sense defective? No doubt, many of them will be false. Some will also be harmful. But many philosophers further argue that prejudiced belief is defective also in the sense that it could only arise from distinctive kinds of epistemic irrationality: we could acquire or retain our prejudiced beliefs only by culpably violating our epistemic responsibilities. Moreover, it is assumed that we are morally responsible for the harms that our prejudiced beliefs cause only because, in forming these beliefs in the first place, we are violating our epistemic responsibilities. This book argues that these common convictions are false and misguided. It shows in detail that there can be plenty of epistemically justified pathways to prejudiced belief. Moreover, it argues that it is a mistake to lean on the concept of epistemic responsibility to give content to ethical responsibilities. In particular, this would unreasonably burden victims of prejudice with having to show that their victimizers were in a position to know better. Accordingly, this book develops an account of moral responsibility for harm which does not depend on finding grounds for epistemic blame. In support of this view, the book offers a number of examples and case studies at individual, collective, and institutional levels of decision making. Additionally, it develops a systematic platform for “non-ideal epistemology” which would apply also to a wide range of other socio-epistemic phenomena of current concern, such as fake news, conspiracy theories, science scepticism, and more.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.027 |
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".