USING A META‐ANALYTIC PERSPECTIVE TO ENHANCE JOB COMPONENT VALIDATION
Bibliographic record
Abstract
This paper develops synthetic validity estimates based on a meta‐analytic‐weighted least squares (WLS) approach to job component validity (JCV), using position analysis questionnaire (PAQ) estimates of job characteristics, and the Data, People, & Things ratings from the Dictionary of Occupational Titles as indices of job complexity. For the general aptitude test battery database of 40,487 employees, nine validity coefficients were estimated for 192 positions. The predicted validities from the WLS approach had lower estimated variability than would be obtained from either the classic JCV approach or local criterion‐related validity studies. Data, People, & Things summary ratings did not consistently moderate validity coefficients, whereas the PAQ data did moderate validity coefficients. In sum, these results suggest that synthetic validity procedures should incorporate a WLS regression approach. Moreover, researchers should consider a comprehensive set of job characteristics when considering job complexity rather than a single aggregated index.
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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 | 0.314 | 0.573 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.022 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".