Bibliographic record
Abstract
Is the fact that a claim comes from a biased source a reason to think the claim false? Obviously it is not a conclusive reason for rejecting the claim. The claim might have been generated independently of the bias, in some epistemically reliable manner. Even if the bias had some causal influence, the claim it generates might in a particular case dovetail with the facts, either accidentally or otherwise. One might suppose, however, that bias is a reason for thinking the claim less likely to be true. Barron cites an argument by Elliott Sober that a person’s belief is implausible if what caused the person to adopt it is independent of whether the belief is true. Barron refutes Sober’s conclusion on Bayesian grounds. If a belief has been produced by a process causally independent of its truth, then by definition the posterior likelihood of arriving at this belief, supposing that the belief is true, [“P(R/Q) ” using Barron’s abbreviations] is equal to the prior likelihood [“P(R)” using Barron’s abbreviations] of arriving at this belief, without making any supposition as to whether the belief is true. That is what it means for the method of arriving at a belief to be causally independent of the truth of the belief. Since the belief has actually been arrived at, the likelihood of arriving at it is not zero. Hence in the statement of Bayes ’ theorem the posterior and
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 | 0.022 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.059 | 0.111 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".