New CSR in the food system: Industry and non-traditional corporate food interests
Bibliographic record
Abstract
Throughout the twentieth century, the food system has not only undergone changes in structure and in process, but has shown a growing transformation in food system governance. Often this transformation involves private actors engaging in the policymaking and governance arena. This paper draws on corporate social responsibility (CSR) as a private governance mechanism that is frequently used by corporate food actors. The rise of industry’s participation in non-traditional corporate food interests (NTCFIs), or social and environmental concerns, will be explored by drawing on changing governance structures in the food system. NTCFIs move beyond traditional interests of corporate actors such as trade, economic regulation, and competitiveness, and reach into social and environmental issues found in the food system that are often a result of agri-food production and its business practices. This paper problematizes the increased CSR of corporate actors in social and environmental issues in the food system. It considers both sides of the debate – an optimistic view of business engaging in NTCFIs, and a more skeptical view. It concludes by stating that given the power and resources of corporate food actors, they should be involved in food system change at arm’s length in a tripartite partnership: civil society, government, and the corporate 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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".