Bibliographic record
Abstract
This chapter reviews some faults of the theoretical literature and findings from the experimental literature on “Gettier” cases. Some “Gettier” cases are so poorly constructed that they are unsuitable for serious study. Some longstanding assumptions about how people tend to judge “Gettier” cases are false. Some “Gettier” cases are judged similarly to paradigmatic ignorance, whereas others are judged similarly to paradigmatic knowledge, rendering it a theoretically useless category. Experimental procedures can affect how people judge “Gettier” cases. Some important central tendencies in judging “Gettier” cases appear to be robust against demographic variation in biological sex, age, language, and culture, although there could be some interesting differences related to culture and personality traits. Some remaining questions regarding Gettier’s cases, “Gettier” cases, and “the Gettier problem” concern the psychology and sociology of contemporary anglophone theoretical epistemology. Some remaining questions regarding the empirical study of knowledge judgments concern mechanisms underlying observed behavioral patterns.
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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 | 0.031 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".