Introducing the Special Section on Openness to Experience: Review of Openness Taxonomies, Measurement, and Nomological Net
Bibliographic record
Abstract
In this introduction to the Special Section on Openness to Experience, we review the historical background of the construct and its measurement. We also provide a meta-analytically based review of its broader nomological net. Specifically, we review relationships with other individual differences constructs, including personality traits, interests, and cognitive ability. We highlight the various roles that openness and intellect play in educational performance, occupational attitudes and behaviors, job performance, career success, and psychological health and well-being. In doing so, we emphasize the unique contributions of the articles published in this special section (Albrecht, Dilchert, Deller, & Paulus; Connelly, Ones, Davies, & Birkland; DeYoung, Quilty, Peterson, & Gray; Roets, Cornelis, & Van Hiel; Woo, Chernyshenko, Longley, Zhang, Chiu, & Stark; Woo, Chernyshenko, Stark, & Conz). Finally, we note fruitful venues for future research involving Openness constructs.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".