Bibliographic record
Abstract
Abstract False beliefs and misleading evidence have striking similarities. In many regards, they are both epistemically bad or undesirable. Yet some epistemologists think that, while one's evidence is normative (i.e., one's available evidence affects the doxastic states one is epistemically permitted or required to have), one's false beliefs cannot be evidence and cannot be normative. They have offered various motivations for treating false beliefs differently from true misleading beliefs, and holding that only the latter may be evidence. I argue that this is puzzling: if misleading evidence and false beliefs share so many important similarities, why treat them differently? I also argue that, given the striking similarities between false beliefs and misleading evidence, many arguments for the factivity of evidence overgeneralize. That is, if these arguments were conclusive, they would also entail that the evidence cannot be misleading. But this is an overgeneralization, since the evidence can be misleading.
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 | 0.029 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.058 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".