The Role of Situational Interviews in Fostering Positive Reactions to Selection Decisions
Bibliographic record
Abstract
We investigated the effect of interview format and employment equity program strength on perceptions of fairness. We used job seekers and vignettes to test the hypotheses. The participants reported lukewarm support for employment equity programs. The use of a situational interview in the selection process of an organisation that had adopted an employment equity program contributed to higher perceptions of fairness vis‐à‐vis the use of an unstructured interview. The results also showed that the inclusion of a situational interview in the selection process mitigated negative reactions to the selection decision when a strong employment equity program was in place as well as when a female visible minority was hired. Nous avons évalué l'impact sur la perception de justice du style d'entretien et de la rigueur d'une charte d'équité relative à l'emploi. On a fait appel à des demandeurs d'emploi et utilisé un test de jugement situationnel pour éprouver les hypothèses. Les répondants se sont montrés peu enthousiastes en ce qui concerne les chartes d'équité relatives à l'emploi. Le choix d'un entretien structuré dans le processus de sélection d'une organisation qui avait adopté une charte d'équité relative à l'emploi a amélioré la perception de justice portant sur un entretien ordinaire. Il apparaît aussi que la présence d'un entretien structuré dans le processus de sélection atténuait les réactions négatives consécutives au résultat de la sélection quand existait une sérieuse charte d'équité relative à l'emploi et quand était embauchée une minorité féminine non négligeable.
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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 | 0.032 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".