The employment selection interview: disparity among research‐based recommendations, current practices and what matters to Human Rights Tribunals
Bibliographic record
Abstract
Abstract We surveyed Human Resources (HR) professionals ( N = 301) on their use of structure in employment interviews. We compared these data with research‐based recommendations and with elements of interview structure considered important by Canadian Human Rights Tribunals. Contrary to research‐informed recommendations, HR practitioners and Human Rights Tribunals appear to attach little importance to job analysis as the foundation for developing interview questions and to interviewer training. While Tribunals and the established research literature on employment interviewing favour standardization, this is of lesser concern to Canadian HR practitioners. Finally, there was convergence on valuing behavioural questions and note‐taking, but not on valuing interview panels. Implications of our findings for research and practice are discussed. Copyright © 2007 ASAC. Published by John Wiley & Sons, Ltd.
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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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 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; both teacher heads 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".