Managing Gender Equity and Equality Across Borders—A Review and Introduction to the Special Issue
Bibliographic record
Abstract
ABSTRACT Achieving gender equality remains a pressing global challenge. In response, many organizations and multinational enterprises (MNEs) have adopted gender diversity management (GDM)—human resource practices aimed at promoting gender equity and equality in the workplace. While prior research highlights the importance of institutional context in shaping the implementation and outcomes of GDM, there is limited understanding of how to contextualize and implement these practices effectively across diverse national settings. In this this editorial, we first review existing research in three key areas: (1) the transfer of GDM practices across MNEs, (2) the gender composition of MNEs’ top management teams, and (3) comparative studies of GDM. Our analysis underscores the limitations of universal, “one‐size‐fits‐all” approaches and emphasizes the need for context‐sensitivity. In this context, we then introduce the contributions to the Special Issue. Together, these articles advance our understanding of the complex interplay between organizational practices and local norms in shaping GDM implementation and outcomes. Finally, we outline research directions that can help propel future work, including the need for a deeper understanding of MNEs’ motivations for engaging in GDM, the positioning of gender within broader diversity agendas, and the implications of growing anti‐DEI sentiment.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".