Introducing the Special Section on Openness to Experience: Review of Openness Taxonomies, Measurement, and Nomological Net
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it