Traders as sustainability governance actors in global food supply chains: A research agenda
Bibliographic record
Abstract
Abstract Corporate actors are rapidly gaining ground as nontraditional forms of authority that shape sustainability governance efforts in global food supply chains. This paper highlights the critical, but underresearched role of traders—companies whose core business lies in the movement and exchange of agricultural commodities between producers and manufacturers—in linking corporate sustainability ambitions to on‐the‐ground impacts. Drawing on a systematic analysis of the major transnational corporations trading cocoa, coffee, and palm oil, we present advantages and potential pitfalls of relying on traders as implementers of sustainability governance and outline a future research agenda that focuses on producer‐level impacts, changes in supply chain organization and power dynamics, and traders' interactions with state and other nonstate actors. At the intersection of supply chain management, political economy, geography, and global governance, research on traders as key sustainability governance actors also provides novel opportunities for interdisciplinary work and stakeholder engagement.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".