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Record W4399906277 · doi:10.1002/csr.2884

Assessing and managing environmental, social, and governance risks in agri‐food companies

2024· article· en· W4399906277 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsÉcole Nationale d'Administration PubliqueUniversité Laval
Fundersnot available
KeywordsBusinessCorporate governanceEnvironmental resource managementFood safetyEnvironmental planningEnvironmental scienceFinanceFood science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.267
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it