Improving Gender Equality in GVCs: A Comparative Analysis of Interventions in Three Countries
Bibliographic record
Abstract
Global value chain (GVC) governance has become an essential part of the international development agenda to promote the inclusion and integration of all GVC's participants at all levels that benefit from the upgrading process. Ensuring gender equality and inclusion at the community level has been overlooked in this agenda, despite the recognition and adaptation of SDG 5 gender equality by governments and businesses worldwide. Previous literature indicates community governance as a supplement GVC governance structure to private, social, and public governance can facilitate livelihood upgrading for rural communities. However, it remains unclear under which conditions both women and men can equally benefit from the livelihood upgrading. To address this gap, this study that draws from GVC governance literature, feminist theory, and institutional theory empirically examined the effect of third-party interventions launched in artisanal mining communities on gender equality and women’s empowerment, particularly the furthest reaches of a GVC when local institutions are weak—using a novel dataset collected from local miners in the Democratic Republic of Congo, Uganda and Zimbabwe. We adopted ten indicators that reflect SDG 5 to investigate the pre-intervention conditions of five mining associations in three countries. Our study offers insights based on evidence-based recommendations for policymakers and practitioners who integrate SDG 5 into their global sustainability strategies. In the form of collective efforts from social and community actors, women and girls can benefit from integrating into the global economy, especially in the first mile of the GVC.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".