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Environmental disclosure in annual reports: A legitimacy theory perspective

2001· article· en· W1507303391 on OpenAlexaffabout
Arline Savage, Elizabeth Gilbert, J Rowlands, a.J. Cataldo

Bibliographic record

VenueSouth African Journal of Accounting Research · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPerspective (graphical)LegitimacyEnvironmental resource managementAccountingPolitical scienceBusinessEnvironmental scienceLawComputer science

Abstract

fetched live from OpenAlex

In recent years, corporations have increasingly used their annual reports to voluntarily disclose information relating to their social actions, particularly those concerning the natural environment. The conventional accounting framework, with its emphasis on decision-usefulness, has largely proved unsatisfactory in explaining this practice, as have various economic theories. This paper uses a legitimacy theory framework to explain why companies engage in this type of voluntary reporting. This research contributes to the accounting literature by advancing legitimacy theory as a framework for examining environmental reporting and applying this in an effort to understand the environmental disclosure practices of two Canadian pulp and paper companies.This paper stems from the lead author's doctoral thesis at the University of Port Elizabeth. An earlier version of the paper was presented at the 1998 Annual Meeting of the American Accounting Association. We thank Nola Buhr, William Cenker, three anonymous reviewers, and research forum participants at Windsor University, Canada; Oakland University, USA; California State University—Bakersfield, USA; Gonzaga University, USA; Cleveland State University, USA; University of New Brunswick—Saint John, Canada; and University of Port Elizabeth, South Africa for their comments on earlier drafts.

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 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)0.017
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.311
Teacher spread0.279 · 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 teacher head, not a consensus.

Study designObservational
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

Citations11
Published2001
Admission routes2
Has abstractyes

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