Environmental Upgrading in the Furthest Reaches of the Global Supply Chain
Bibliographic record
Abstract
The shortcomings in environmental stewardship within institutionally weak and socially fragile contexts, such as artisanal and small-scale mining communities (ASM), epitomize the grand challenges identified by the UN Sustainable Development Goals of providing clean water and reducing pollution. As multinational enterprises (MNE) become increasingly responsible for the entirety of their global value chains (GVC), managers become highly motivated to address these issues. However, the primary approach of using cascading compliance appears ineffective in the furthest reaches (i.e., the “first mile”) of GVCs. Therefore, we ask what alternative tactics can upgrade environmental stewardship in the first mile of GVCs that often consist of informal suppliers. Extending Kano (2018), we adopt a more inclusive approach than typically found in international business research, using a relational GVC governance lens that engages non-traditional entities, e.g., informal suppliers. Based on a rare dataset of African gold ASM and using fuzzy-set Qualitative Comparative Analysis (fsQCA), we find multiple pathways to improved environmental stewardship, thereby demonstrating novel, feasible solutions to address environmental grand challenges in the furthest reaches of GVCs. By assessing an NGO’s intervention that engages informal suppliers, we demonstrate that environmental upgrading is attainable even in those challenging contexts.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".