Bibliographic record
Abstract
Abstract Epistemic Courage, by Jonathan Ichikawa, argues that many mainstream ideas about what to believe encode a bias towards the skeptical: we devote a lot of thought and attention to the possible mistake of believing things when we shouldn’t; we ought, Ichikawa argues, to think much more than we do about the possible mistake of not believing things when we should. Mistaken suspension of judgment can be just as irrational, and just as morally and politically unfortunate, as mistaken belief can. Widespread assumptions to the contrary, Ichikawa says, encode a conservative political ideology, motivating an undue deference for the status quo. Throughout the book, Ichikawa uses a wide range of engaging and timely examples to illustrate his points, focusing both on everyday practical cases and on morally weighty and politically controversial ones, like conspiracy theories, medical misinformation, and rape culture. Epistemic Courage demonstrates that epistemology is no mere academic abstraction—the question of what to believe couldn’t be more urgent.
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 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.030 |
| Insufficient payload (model declined to judge) | 0.035 | 0.148 |
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; both teacher heads agree on what is shown here.
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".