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 C anada. 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 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".