Stop Violence in GVCs! A Critical Analysis of Interventions to Reduce Gender-based Violence
Bibliographic record
Abstract
As highlighted in the United Nations Sustainable Development Goals, addressing gender-based violence (GBV) has been identified not only as an essential avenue to achieve gender equality and better health but also as a way of “doing good” by multinational corporations (MNCs). While lead MNCs of global value chains (GVC) are pressed to improve social and environmental outcomes through GVCs governance, it remains a theoretical and empirical question as to the conditions under which various interventions are effective. Our study builds on the GVC social upgrading literature, status inconsistency theory, and social identity theory to examine the effects of specific initiatives to address GBV among artisanal mining communities in the D.R. of Congo by using a novel longitudinal dataset collected in 2019 and 2020. Our results show that interventions that incorporate educational components of GBV-related knowledge and promote social inclusion are more likely to reduce GBV in the furthest reaches of a GVC when local institutions are weak. However, these positive influences of these interventions diminished during the COVID-19 pandemic. These findings shed new light on the theoretical implications of GVC performance and practical guidance for project and policy design to combat GBV.
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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 | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".