Assessing and managing environmental, social, and governance risks in agri‐food companies
Bibliographic record
Abstract
Abstract The objective of this article is to analyze the environmental, social, and governance (ESG) risks to which agri‐food companies are exposed and the various practices they adopt to manage them. An analysis of the sustainability reporting produced by 135 agri‐food companies that are relatively committed to ESG risk management shows the wide diversity of ESG risks they consider as well as the very uneven coverage of these risks in corporate disclosures. This article proposes an integrative model to describe how agri‐food companies handle risk management based on four main topics: assessing and monitoring ESG risks; internalizing risk management; implementing standards, approaches, and specific tools; and preventing risks through innovation and stakeholder partnerships. This article makes important contributions to the emerging literature on ESG risk management and corporate sustainability in the agri‐food industry, notably by mapping such risks and by summarizing the main practices used by agri‐food companies to manage them.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".