Measuring intellectual humility through situated behavior: An alternative to dispositional self-reports
Bibliographic record
Abstract
Traditional measurement approaches assume that metacognitive features like perspective-taking, open-mindedness, or intellectual humility manifest as stable dispositions. Focusing on intellectual humility (IH)—recognizing the limits of one’s knowledge and fallibility—we proposed an alternative situated approach, testing it across diverse populations: English- and Spanish-speaking North Americans (N=633) and adults from 136 rural Honduran villages (N=2,567). Participants recalled three recent disagreements from their lives–one freely chosen, one where they were wrong, one where they were right—and reported specific epistemic behaviors (e.g., careful listening, considering others’ views) through binary-chained probes to avoid numeric rating scales. This method revealed coherence of the IH construct across cultures, while varying substantially across situations. Most variance (74-81%) occurred within (vs. between) people across situations: heightened when recalling being wrong versus right and, in Honduras, when disagreeing with higher-status partners. Situational effects also fully explained gender differences in the Honduran sample. The situated approach showed efficiency outside Western and educated contexts and helps overcome the humility paradox—when least intellectually humble overclaim their humility while more humble individuals underestimate it. We discuss how the methodological principles we apply here—focus on behavioral expression of target characteristics in specific situations (vs. self-reports of one’s abstract tendencies), sampling from actual experiences (vs. hypothetical scenarios), decomposing numerical scales into binary-chain prompts to increase accessibility across diverse populations, and explicitly modelling intra-individual variability—offers a framework for assessing other metacognitive and self-regulatory constructs across diverse populations, beyond abstract dispositional self-views dominating psychological literature.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".