Opening Up Openness: A Theoretical Sort Following Critical Incidents Methodology and a Meta-Analytic Investigation of the Trait Family Measures
Bibliographic record
Abstract
Existing taxonomies of Openness's facet structure have produced widely divergent results, and there is limited comprehensive empirical evidence about how Openness-related scales on existing personality inventories align within the 5-factor framework. In Study 1, we used a critical incidents sorting methodology to identify 11 categories of Openness measures; in Study 2, we meta-analyzed the relationships of these categories with global markers of the Big Five traits (utilizing data from 106 samples with a total sample size of N = 35,886). Our results identified 4 true facets of Openness: aestheticism, openness to sensations, nontraditionalism, and introspection. Measures of these facets were unadulterated by variance from other Big Five traits. Many traits frequently conceptualized as facets of Openness (e.g., innovation/creativity, variety-seeking, and tolerance) emerged as trait compounds that, although related to Openness, are also dependent on other Big Five traits. We discuss how Openness should be conceptualized, measured, and studied in light of the empirically based, refined taxonomy emerging from this research.
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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 | 0.074 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".