Validation Is Like Motor Oil: Synthetic Is Better
Bibliographic record
Abstract
Although synthetic validation has long been suggested as a practical and defensible approach to establishing validity evidence, synthetic validation techniques are infrequently used and not well understood by the practitioners and researchers they could most benefit. Therefore, we describe the assumptions, origins, and methods for establishing validity evidence of the two primary types of synthetic validation techniques: (a) job component validity and (b) job requirements matrix. We then present the case for synthetic validation as the best approach for many situations and address the potential limitations of synthetic validation. We conclude by proposing the development of a comprehensive database to build prediction equations for use in synthetic validation of jobs across the U.S. economy and reviewing potential obstacles to the creation of such a database. We maintain that synthetic validation is a practically useful methodology that has great potential to advance the science and practice of industrial and organizational psychology.
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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 | 0.148 | 0.489 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".