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 The fact that each of us has significantly greater confidence in the claims of co-partisans – those belonging to groups with which we identify – explains, in large part, why so many people believe a significant amount of the misinformation they encounter. It's natural to assume that such misinformed partisan beliefs typically involve a rational failure of some kind, and philosophers and psychologists have defended various accounts of the nature of the rational failure purportedly involved. I argue that none of the standard diagnoses of the irrationality of misinformed partisan beliefs is convincing, but I also argue that we ought to reject attempts to characterize these beliefs as rational or consistent with epistemic virtue. Accordingly, I defend an alternative diagnosis of the relevant epistemic error. Specifically, I maintain that such beliefs typically result when an individual evaluating testimony assigns more weight to co-partisanship than he ought to under the circumstances, and consequently believes the testimony of co-partisans when better alternatives are available.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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