Bibliographic record
Abstract
Abstract Defeat cases are often taken to show that even the most securely based judgment can be rationally undermined by misleading evidence. Starting with some best-case scenario for perceptual knowledge, for example, it is possible to undermine the subject’s confidence in her sensory faculties until it becomes unreasonable for her to persist in her belief. Some have taken such cases to indicate that any basis for knowledge is rationally defeasible; others have argued that there can be unreasonable knowledge. I argue that defeat cases really involve not an exposure of weakness in the basis of a judgment, but a shift in that basis. For example, when threatening doubts are raised about whether conditions are favorable for perception, one shifts from a basis of unreflective perceptual judgment to a basis of conscious inference. In these cases, the basis of one’s knowledge is lost, rather than rationally undermined. This approach to defeat clears the path for a new way to defend infallibilism in epistemology, and a new understanding of what can count as the basis of any instance of knowledge.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".