Pro‐diversity practices and perceived insider status
Bibliographic record
Abstract
Purpose The aim of this paper is to study the effects of pro‐diversity practices on perceived insider status, and explore the moderating role of leader‐member exchange in this relationship. The main and interactive effects on PIS are studied for cultural minority and majority groups. Design/methodology/approach Research hypotheses are tested with a questionnaire administered to 210 employees working in three Canadian organizations engaged in diversity management. Findings Results indicate that the main and interactive effects of organizational fairness and leader‐member exchange on perceived insider status are significant. The interactive effect on perceived insider status is higher for cultural minorities than for other employees. Research limitations/implications This study shows the importance of perceived insider status in the field of diversity, identifies organizational fairness and leader‐member exchange as two significant organizational antecedents to perceived insider status, and describes the mechanisms linking these antecedents to perceived insider status (the interaction effects). Originality/value The main contribution of the research resides in the identification of perceived insider status as a variable that deserves more attention in the field of diversity. The article invites future research to explore the behavioral consequences of perceived insider status in diverse teams, and to pursue the understanding of mechanisms leading to feelings of inclusion.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".