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Record W2017080666 · doi:10.1177/0007650315576134

Is Sustainability Performance Comparable? A Study of GRI Reports of Mining Organizations

2015· article· en· W2017080666 on OpenAlexaff
Olivier Boiral, Jean‐François Henri

Bibliographic record

VenueBusiness & Society · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComparabilitySustainabilityContingencyBusinessMeaning (existential)Sustainability reportingContingency theoryContent analysisImpossibilityAccountingKnowledge managementComputer sciencePolitical scienceSociologyPsychologySocial science

Abstract

fetched live from OpenAlex

The objective of this study is to analyze the measurability and interfirm comparability of sustainability performance through the qualitative content analysis of 12 sustainability reports of mining firms using the Global Reporting Initiative (GRI) guidelines. The systematic comparison of information disclosed in 92 GRI indicators sheds light on the reasons underlying the impossibility of rigorously measuring and comparing the sustainability performance of firms from the same sector, which are supposed to be strictly following the same reporting guideline. These reasons include qualitative aspects of sustainability, lack of compliance with GRI protocols, indicator contingency, ambiguous or incomplete information, data heterogeneity, and report opacity. The study makes it possible to return to the very notion of sustainability, its meaning, and flexible application by organizations. The results are discussed from three different theoretical perspectives (functionalist, critical, and postmodern), each of which proposes possible and complementary explanations of the main findings.

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)0.051
metaresearch head score (Gemma)0.238
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.238
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0020.007
Scholarly communication0.0040.006
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.279
Teacher spread0.238 · 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.

Study designObservational
DomainReporting
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

Citations249
Published2015
Admission routes1
Has abstractyes

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