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Record W2141158220 · doi:10.5539/jms.v4n1p42

Corporate Water Risk: A Critique of Prevailing Best Practice

2014· article· en· W2141158220 on OpenAlexvenueno aff
Alex Money

Bibliographic record

VenueJournal of Management and Sustainability · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEconomics

Abstract

fetched live from OpenAlex

This paper considers corporate water risk from the perspective of company disclosure. An empirical study, it reviews 6 years’ disclosure for 58 companies in the global consumer staples sector. Drawing on a conceptual framework of institutional theory and resource dependence, it examines the disclosed yardsticks by which multinational companies measure their management of water risk. The first empirical study of its kind, it suggests that companies target future improvements that are generally less aspirational than their historic achievements. This appears to be a function of diminishing marginal returns on efficiency investment, exacerbated by a rational reluctance to venture beyond the ‘fence line’. The evidence suggests that corporate water risk is increasingly viewed as a political rather than operational issue within the disclosure matrix. Current perceptions of best practice are entrenching a status quo that is fundamentally unfit for purpose given the scale of the challenges that need to be addressed over the rest of this decade, and beyond.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.089
metaresearch head score (Gemma)0.126
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.089
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0890.126
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0110.008
Science and technology studies0.0070.091
Scholarly communication0.0220.021
Open science0.0080.009
Research integrity0.0190.028
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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