The Power of Big Box Retail in Global Environmental Governance: Bringing Commodity Chains Back into IR
Bibliographic record
Abstract
This article focuses on analysing the consequences for global governance of the growing power of the world’s biggest retailers, illustrating with the case of global forest governance. It argues that the rising power of big retail within global commodity chains is creating both significant challenges — and some opportunities — for global environmental governance. The analysis suggests a need for IR to focus more on the shifting political power of multinational corporations, as both barriers to, and progress in, the governance of complex global issues such as deforestation and climate change increasingly occur in the corporate sphere. More specifically, the authors see great value in bringing research on globalising commodity chains back into IR, first revealing the power dynamics within these chains, then building on this to analyse the implications for global change and world politics. This reinforces and complements the message in Bernstein et al. (in this volume) that understanding the future of global climate governance must include the complex interactions between transnational governance practices and interstate negotiations. But it also suggests a need for IR scholars to go even further to unpack the consequences of how the shifting power dynamics of governance practices within the corporate sphere are intersecting — or running parallel — with more overarching multilateral and transnational environmental processes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".