Epistemic Evaluation: Purposeful Evaluation By David K. Henderson and John Greco
Bibliographic record
Abstract
What is the point of epistemic evaluation? Why do we appraise others as knowers, understanders and so forth? Epistemology has traditionally focused on analysing the conditions under which one has knowledge, leaving aside for the most part questions about the roles played by epistemic evaluation in our lives more broadly. This fact is borne out by the so-called Gettier literature. For decades, epistemologists have attempted to ferret out the necessary and sufficient conditions for knowledge, but few have asked why knowledge would have (or lack) the features suggested by conceptual analysis. Suppose, for example, that knowledge really is non-lucky justified true belief. Why would this be? What use do we have for a concept that is demarcated by those conditions? Is there something abhorrent about coming by true beliefs in a fortuitous fashion? Epistemic Evaluation, edited by David Henderson and John Greco, foregrounds these broader questions about the role and importance of epistemic evaluation in human life. This volume explores a way of doing epistemology called ‘purposeful epistemology’. A purposeful epistemologist investigates what our epistemic concepts, norms, and practices are for. Beyond throwing light on the nature, value, and purpose of our epistemic concepts, norms, and practices, this approach might help us make headway on a variety of thorny philosophical issues, as I’ll describe below.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".