A Review of Canadian Human Rights Cases Involving the Employment Interview
Bibliographic record
Abstract
Abstract The goal of this study was to assess the extent to which the scholarly literature on employment interviewing is reflected in the deliberations and decisions of Human Rights Tribunals in Canada. We reviewed human rights cases reported in the Canadian Human Rights Reporter from 1980 to 2003. All cases involving charges of discrimination alleged to have occurred during a face‐to‐face employment interview were included for analysis (N = 75). Findings suggest that while tribunals give great importance to the standardization of the entire interview process across all candidates, they largely neglect the importance of job analysis as the foundation for job descriptions and interview questions. Résumé Dans cette étude, nous examinons les cas de violation aux droits de la personne publicés entre 1980 et 2003 dans la revue Canadian Human Rights Reporter. Nous analysons plus précisément les cas relatifs aux plaintes pour discrimination (N = 75) enregistrés lors des entrevues d'emploi directes. L'objet de I'étude est de déterminer jusqu'à quel point les contributions contenues dans la littérature savante sur les entrevues d'emploi sont prises en compte dans les délibérations et les décisions des tribunaux canadiens des droits de la personne. L étude montre que bien que les tribunaux accordent une grande importance à la standardisation de tout le processus de I'entrevue, ils sous‐estiment grandement le rôle que I'analyse de I'emploi peut jouer dans la description des postes et les questions d'entrevue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.053 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".