Mega-Mergers on the Menu: Corporate Concentration and the Politics of Sustainability in the Global Food System
Bibliographic record
Abstract
The agricultural input industry has become more concentrated in the wake of recently announced corporate mergers in the sector. This article examines the environmental implications of corporate concentration in the agricultural input sector and outlines the challenges of establishing effective international policy and governance on this issue. The article makes two arguments. First, corporate concentration matters for food system sustainability. Consolidation in the global seed and agro-chemical industries has been deeply entwined with the rise of industrial agriculture, which has been associated with a host of environmental problems including an increase in agro-chemical use and the loss of agricultural biodiversity. Second, although corporate concentration has important sustainability implications, there is little recognition of the potential connection between these issues in international governance measures. The article outlines a number of factors that discourage the development of policy and governance on these issues, including the lack of a clear scientific consensus on how best to promote sustainable agriculture; the weak and fragmented nature of regulatory frameworks and institutions that oversee competition policy and food system sustainability; the power of agribusiness firms to influence policy outcomes; and the complex and distanced nature of the underlying drivers of corporate concentration in the sector.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".