Diversity and Inclusion (D&I) through HRM: Country Perspectives
Bibliographic record
Abstract
In this presenter symposium, we intend to discuss the potentials and barriers of diversity and inclusion (D&I) through human resource management (HRM) from single and cross-country perspectives. The symposium includes four papers that consider diversity and inclusion from different angles and diverse main foci. The first paper (Klarsfeld) attempts to frame where comparative research is heading to, drawing on previous and current literature and an analysis of research gaps. The next paper focuses on diversity and inclusion in a specific country, Canada (Ng, Lyons and Schweitzer), another two take a country-comparative stance, United States and France (Merriweather Woodson and Ollier-Malaterre) and Germany and Turkey (Kornau, Sieben and Knappert). While the paper on Canada focuses on the intersection of gender and immigration status regarding millennials (Ng et al.), the remaining two papers examine the historical, legal and political framings of diversity and inclusion in the examined countries, as well as their impact on research approaches and diversity management concepts such as intersectionality (Merriweather Woodson and Ollier-Malaterre) and on the moves, strategies and barriers of actors wanting to promote equality and diversity at work (Kornau et al.). Taken together, the papers give rich insights into the contextual underpinnings of diversity and inclusion and/or on the study of intersectionality in various contexts. Theoretical and practical implications will be derived through cross-readings and cross-discussions, as well as through interactive group discussions. Implications of Gender and Immigration Status on the Career of Millennials Presenter: Eddy S. Ng; Dalhousie U. Presenter: Sean Thomas Lyons; U. of Guelph Presenter: Linda Schweitzer; Carleton U. An Intersectional Approach to Diversity Management in the United States and France Presenter: Tarani Joy Merriweather Woodson; Columbia U. Presenter: Ariane Ollier-Malaterre; UQAM International and Comparative Perspectives on Diversity and Equal Treatment Presenter: Alain Klarsfeld; Toulouse Business School The Political Arena of Equality and Diversity at Work: The Cases of Germany and Turkey Presenter: Angela Kornau; Helmut Schmidt U. Presenter: Barbara Sieben; Helmut-Schmidt U. Presenter: Lena Knappert; Tilburg U.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".