Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it