Bibliographic record
Abstract
> 2,500 studies, we present the most comprehensive, quantitative review and synthesis of the occupational effects of C available in the literature. Results show C has effects in a desirable direction for 98% of variables and a grand mean of [Formula: see text] (SD = 0.13), indicative of a potent, pervasive influence across occupational variables. Using the top 33% of effect sizes [Formula: see text] we synthesize 10 characteristic themes of C's occupational functioning: 1) motivation for goal-directed performance, 2) preference for more predictable environments, 3) interpersonal responsibility for shared goals, 4) commitment, 5) perseverance, 6) self-regulatory restraint to avoid counterproductivity, and 7) proficient performance-especially for 8) conventional goals, 9) requiring persistence. Finally, we examine C's relation to performance across 8 occupations. Results indicate that occupational complexity moderates this relation. That is, 10) high occupational complexity versus low-to-moderate occupational complexity attenuates the performance effect of C. Altogether, results suggest that goal-directed performance is fundamental to C and that motivational engagement, behavioral restraint, and environmental predictability influence its optimal occupational expression. We conclude by discussing applied and policy implications of our findings.
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 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".