Registered Replication Report: A Large Multilab Cross-Cultural Conceptual Replication of Turri et al. (2015)
Bibliographic record
Abstract
According to the justified true belief (JTB) account of knowledge, people can truly know something only if they have a belief that is both justified and true (i.e., knowledge is JTB). This account was challenged by Gettier, who argued that JTB does not explain knowledge attributions in certain situations, later called “Gettier-type cases,” wherein protagonists are justified in believing something to be true, but their belief was correct only because of luck. Laypeople may not attribute knowledge to protagonists with justified but only luckily true beliefs. Although some research has found evidence for these so-called Gettier intuitions, Turri et al. found no evidence that participants attributed knowledge in a counterfeit-object Gettier-type case differently than in a matched case of JTB. In a large-scale, cross-cultural conceptual replication of Turri and colleagues’ Experiment 1 ( N = 4,724) using a within-participants design and three vignettes across 19 geopolitical regions, we did find evidence for Gettier intuitions; participants were 1.86 times more likely to attribute knowledge to protagonists in standard cases of JTB than to protagonists in Gettier-type cases. These results suggest that Gettier intuitions may be detectable across different scenarios and cultural contexts. However, the size of the Gettier intuition effect did vary by vignette, and the Turri et al. vignette produced the smallest effect, which was similar in size to that observed in the original study. Differences across vignettes suggest that epistemic intuitions may also depend on contextual factors unrelated to the criteria of knowledge, such as the characteristics of the protagonist being evaluated.
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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 | 0.035 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.077 | 0.032 |
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".