Epistemic buck-passing and the interpersonal view of testimony
Bibliographic record
Abstract
Abstract Two ideas shape the epistemology of testimony. One is that testimony provides a unique kind of knowledge. The other is that testimonial knowledge is a social achievement. In traditional terms, those who affirm these ideas are anti-reductionists, and those who deny them are reductionists. There is increasing interest, however, in the possibility of affirming these ideas without embracing anti-reductionism. Thus, Sanford Goldberg uses the idea of epistemic buck-passing to argue that even reductionists can accept the uniqueness of testimonial knowledge, and Jennifer Lackey gives both speaker and hearer an essential role in testimonial justification, rejecting both reductionism and anti-reductionism in favor of what she calls dualism. After distinguishing a weaker and a stronger notion of buck-passing, we show how anti-reductionists can use the stronger notion to motivate their position, and to answer Goldberg’s and Lackey’s challenges.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".