Bibliographic record
Abstract
Abstract This chapter has four central aims. Section 1 distinguishes two ideas within epistemology that sometimes travel under the name “contextualism”—the “situational contextualist” idea that an individual’s context, especially their social context, can make for a difference in what they know, and the “linguistic contextualist” idea that discourse using the word “knows” and its cognates is context-sensitive, expressing different contents in different conversational contexts. Second, section 2 situates contextualism with respect to several influential ideas in feminist epistemology. These ideas are thoroughgoingly contextualist in the situational sense; this section explores the prospects for linguistic contextualist analogues or implementations of them. Simple connections between these feminist ideas and linguistic contextualism will prove elusive, but more subtle ones are possible, and sometimes attractive. Section 3 considers the degree to which contextual epistemic parameters are determined interpersonally, as opposed to individualistically. Should contextualists hold that speakers can individually determine the contextual parameters that influence the truth-conditions of their utterances? Or are they fixed at a broader social level? This section rehearses some influential reasons to opt for the latter, more social, form of contextualism. Section 4 discusses the practical and moral significance of speakers’ choices of epistemic parameters, given contextualism. For example, it considers how standards-raising can be used to discredit evidential sources, with an eye toward the social and moral consequences of such moves.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.069 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".