A study of individual values and employment equity in Canada, France and Ireland
Bibliographic record
Abstract
Purpose The purpose of this paper, in the context of the employment equity (EE) field, is to explore the relationship between individual values/beliefs and simulated hiring decisions of minority candidates in Canada, France and Ireland. Design/methodology/approach Individual values/beliefs were elicited using Likert type scales; subjects responded to a series of simulated hiring scenarios. Findings The link between individual value and belief systems and EE‐related HR decision making on recruitment of minority candidates is modestly supported by the findings presented here. The values/beliefs of students from leading business schools influenced, if in part, their simulated hiring decisions on minority candidates presented in the scenarios. National context also matters as EE institutions differ at the societal level of analysis. Research limitations/implications The subjects were business school students of limited work experience addressing scenario situations, not practicing managers making real hiring decisions. The use of self‐reports leads to the usual issues related to common method variance, the consistency motif, social desirability bias, and so on and we note the limits due to the reverse ecological fallacy. Research findings provide modest support to this argument but should be treated with caution. Practical implications Individual values and beliefs matter in HR decision making on recruitment of minority candidates. Originality/value Much EE research focuses on antecedents of values/beliefs; this paper is one of a handful of investigations that attempts to establish possible outcomes of values/beliefs towards EE.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".