Knowledge and Luck...and Stereotypes?: Examining the Influence of an Actor’s Group Membership on Knowledge Attribution
Bibliographic record
Abstract
According to Justified True Belief Theory, a person can be said to know something if they arrive at true beliefs for justifiable reasons. Philosophers now mostly agree, however, that this account is inadequate because it fails to capture instances in which people arrive at true beliefs for reasons that, while justifiable, are not the real reasons why the belief is true (i.e., people sometimes get lucky). But when ordinary perceivers attribute knowledge, do they really distinguish between being right for the right reasons and being right because one “just got lucky”? Thus far, empirical work on this question has been mixed. And, in focusing almost exclusively on perceivers’ (lack of) sensitivity to the particular reasons underlying a person’s belief, this work has not yet examined the potential impact of additional aspects of perceiver’s beliefs about a protagonist on the process of knowledge attribution. In this preregistered, multi-site study, we contribute to this literature by replicating and extending the influential work of Turri et al. (2015). Our results showed that participants did attribute knowledge more readily to protagonists who were right for the right reasons than to protagonists who were right because they got lucky. Further, this tendency held regardless of the protagonist’s level of expertise about the domain of knowledge in question, suggesting that logical considerations about the reasons underlying a protagonist’s beliefs may override at least some category-based beliefs about the protagonist.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".