Bibliographic record
Abstract
Abstract As a general rule, whenever a hearer is justified in forming the belief that p on the basis of a speaker’s testimony, she will also be justified in assuming that the speaker has formed her belief appropriately in light of a relevantly large and representative sample of the evidence that bears on p . In simpler terms, a justification for taking someone’s testimony entails a justification for trusting her assessment of the evidence. This introduces the possibility of what I will call “evidential preemption.” Evidential preemption occurs when a speaker, in addition to offering testimony that p , also warns the hearer of the likelihood that she will subsequently be confronted with apparently contrary evidence: this is done, however, not so as to encourage the hearer to temper her confidence in p in anticipation of that evidence, but rather to suggest that the (apparently) contrary evidence is in fact misleading evidence or evidence that has already been taken into account. Either way, the speaker is signalling to the hearer that the subsequent disclosure of this evidence will not require her to significantly revise her belief that p . Such preemption can effectively inoculate an audience against future contrary evidence, and thereby creates an opening for a form of exploitative manipulation that I will call “epistemic grooming.” Nonetheless, I argue, not all uses of evidential preemption are nefarious; it can also serve as an important tool for guiding epistemically limited agents though complex evidential scenarios.
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 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".