Beyond the numbers: Reflections from three Global South countries using the global gender gap index
Bibliographic record
Abstract
The World Economic Forum's (WEF) Global Gender Gap Index (GGGI) has become synonymous with the measurement of gender equality. However, the measures included in calculating the GGGI are driven mainly by development agencies' agendas. Notably, in developing countries, where gender inequalities run deep, governments' efforts to address these inequalities at the macro-national level have limited influence on gender diversity management (GDM) policies at the meso-organizational and micro-individual levels. To provide evidence, we applied the multilevel relational framework to review the state of gender (in)equality in three Global South countries—Bangladesh, India, and Mexico—using GGGI. Our review revealed that the GGGI tells a numerical story with political appeal, is used to support policies and programs that satisfy the criteria of developmental and donor agencies, and puts a checkmark next to the governmental agenda to meet Sustainable Development Goals (SDGs) at the macro-national level. These macro-national policies and programs have limited success in informing actionable human resources (HR) policies and practices to address gender (in)equality at meso-organizational and micro-individual levels. While the GGGI does not claim to measure the root cause of gender inequality, this study aimed to highlight the GGGI's impact on closing the gender gap. This paper contributes to the narrow body of literature that has brought the conversation on the SDGs and the global gender gap in management and organization literature. The present study also has theoretical and policy implications, as we provide a critical review of the GGGI and recommend including indicators to measure GGGI subindices that can then better inform meaningful and effective interventions at the macro-level and consequent GDM policies for achieving gender equity at the meso-level and women's empowerment at the individual micro-level.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".