Predicting Training and Job Performance for Transit Operators
Bibliographic record
Abstract
Although considerable meta‐analytic research has validated the use of cognitive ability tests, structured interviews, and personality tests with training and job performance criteria, few studies have investigated the validity of these measures with transit operators. There are the only two single studies of concurrent validation research specifically with transit operators. This article presents the results of a predictive validation study conducted with transit operator applicants for a large urban transit authority in Canada. Key knowledge, skills, and abilities were determined for the role and used as a basis for the design and choice of predictors and criteria. Four predictors were used in the study: education, cognitive ability, personality assessment, and structured interview. Criteria included training performance (formative and summative), probationary performance, preventable accidents, and lost time injuries. Validation results supported cognitive ability, structured interview, and several personality factors as predictors of training performance, but less so for job performance. The use of formative training ratings greatly augmented the evidence supporting the predictors beyond typical organizational criteria.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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; 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".