Managing Gender Equity and Equality Across Borders—A Review and Introduction to the Special Issue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it