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Record W4387445427 · doi:10.31234/osf.io/9bscz

Measuring intellectual humility through situated behavior: An alternative to dispositional self-reports

2023· preprint· en· W4387445427 on OpenAlexaff
Maksim Rudnev, Ana Lucia Rodriguez de la Rosa, Ryan Barrett, Nicholas A. Christakis, Igor Grossmann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychologyHumilitySocial psychologySituational ethicsContext (archaeology)Active listeningSet (abstract data type)Openness to experiencePolitical scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.256
GPT teacher head0.416
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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