The neutralization of ESG risks by leading agri-food companies
Bibliographic record
Abstract
Purpose The purpose of this paper is to shed more light on the motivations for environmental, social and governance (ESG) risk management by agri-food companies and the neutralization techniques used to legitimize the measures taken in this area. Design/methodology/approach Based on an analysis of the sustainability reporting of 135 companies, this study shows the interdependence between the main motivations for ESG risk management and the neutralization techniques used in disclosing information about their exposure to threats or negative events that could damage their image. Findings The results of the study allow us to understand the four main complementary neutralization techniques used to obfuscate the negative consequences of risks related to agri-food activities: mitigating ESG threats, addressing global risks through corporate leadership, taking advantage of sustainability trends and turning risks into opportunities. Practical implications Managers can use the results of this paper to identify the best management approaches to take ESG risks into account more substantially in their company. Social implications Ultimately, this study is important to improve the practices of agri-food companies and therefore their social legitimacy. Originality/value The examination of these neutralization techniques and their underlying motivations makes important contributions to the emerging literature on ESG risk management. The study also contributes to research on the disclosure of negative information that can damage a company’s reputation and on the strategies that companies use to promote the social acceptability of their activities.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".