Bibliographic record
Abstract
Abstract Knowledge attributions—that is, statements of the form “S knows [/doesn’t know] that p”—are a rich target for social-epistemological inquiry. This is not merely because they deploy epistemic vocabulary within an essentially social medium (language). More significantly, it is because knowledge attributions are intentional acts undertaken for specific practical and communicative purposes, against the background of certain shared assumptions and mutual expectations, by socially, culturally, and otherwise situated subjects. Such attributions have long played a key role in epistemological inquiry: as most epistemologists see it, they are an essential source of data to which their theorizing must be responsive. But it’s only recently that knowledge attributions have themselves come in for intense theoretical scrutiny. This chapter focuses on two issues emerging from this study that are of particular social-epistemological interest, and the connections between them. The first, which was prompted by the emergence of epistemic contextualism (and the subsequent articulation of various rivals), concerns the truth-conditional contents of sentences used to attribute knowledge and whether these are interestingly sensitive to social factors. The second, which is owing chiefly to Edward Craig’s attempt to provide a “practical explication” of the concept of knowledge, concerns the social role(s) of knowledge attributions—what extra-individualistic function(s) or purpose(s) they might serve, and what light this might shed on central epistemic concepts and phenomena. One of the main lessons that emerges is that there is substantial overlap between why knowledge attributions are of obvious social-epistemological interest and why their proper theoretical handling is as challenging as it is.
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".