Understanding Gender Equality Initiatives and Supply Chain Resilience in Sustainability Practices
Bibliographic record
Abstract
Abstract This qualitative research investigates the intersectionality of gender equality initiatives and supply chain resilience within sustainability practices. Through in-depth interviews and thematic analysis, the study explores the integration of gender considerations into supply chain management, challenges and opportunities in promoting gender equality, and the impact of gender equality initiatives on supply chain resilience. The findings reveal the importance of fostering gender diversity, inclusivity, and empowerment within supply chains to enhance adaptive capacity, innovation, and overall performance. Despite significant challenges, collaborative partnerships, leadership commitment, and systemic change offer avenues for advancing gender equality and resilience. Moving forward, efforts to integrate gender considerations into supply chain resilience strategies must be accompanied by robust data collection, monitoring, and evaluation mechanisms to assess progress and track performance. Standardized indicators and benchmarks are essential for demonstrating the business case for investing in gender equality within supply chains and driving meaningful change. Overall, this research contributes to a deeper understanding of the complex dynamics between gender, resilience, and sustainability, offering valuable implications for theory, practice, and policy development in the fields of sustainability management, supply chain resilience, and gender studies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".