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Record W1913410153 · doi:10.1002/csr.1323

Linkages between Corporate Sustainability Reporting and Public Policy

2013· article· en· W1913410153 on OpenAlexaffabout
Dan Beare, Ruvena Buslovich, Cory Searcy

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSustainabilitySustainability reportingBusinessGovernment (linguistics)Public policySustainability organizationsCorporationCorporate sustainabilitySocial sustainabilityAccountingPublic relationsCorporate social responsibilityPolitical scienceEconomicsFinanceEconomic growth

Abstract

fetched live from OpenAlex

ABSTRACT The purpose of this paper is to explore the linkages between corporate sustainability reporting and public policy. Interviews with experts from 35 different Canadian corporations that produce a sustainability report were held to address this issue. The interviews specifically focused on exploring how public policy influences sustainability reporting, investigating how corporate sustainability reporting influences public policy, and identifying the barriers to linking sustainability reporting with public policy. The majority of participants explained that their corporation's sustainability reporting has not been heavily influenced by public policy. Even in the relatively few cases where the participating corporations were required to report on sustainability‐related information (i.e. financial and insurance companies), there was little indication that public policy was strongly considered in reporting. Although several participants felt that their sustainability reports could or should influence public policy, there were also indications that corporations are looking for additional guidance on reporting from government. In fact, the lack of direction from government was cited as a key barrier to improved linkages between corporate sustainability reporting and public policy. Future research should focus on addressing this problem, particularly at the individual sector level. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.002
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 head0.050
GPT teacher head0.261
Teacher spread0.211 · 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

Citations60
Published2013
Admission routes2
Has abstractyes

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