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Record W2957839954 · doi:10.3390/su11143825

Reporting within the Corridor of Conformance: Managerial Perspectives on Work Environment Disclosures in Corporate Social Responsibility Reporting

2019· article· en· W2957839954 on OpenAlexaff
Shane M. Dixon, Cory Searcy, Patrick Neumann

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsToronto Metropolitan UniversityWilfrid Laurier University
Fundersnot available
KeywordsCorporate social responsibilityBusinessWork (physics)Public relationsAccountingAffect (linguistics)LegitimacyPsychologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

This paper examines managerial perspectives on work environment (WE) disclosures in corporate social responsibility (CSR) reports. WE encompasses all aspects of the design and management of the work system that affect the employees’ interactions with the workplace. The data are drawn from interviews with 20 CSR managers in large companies that are recognized as high performers in CSR. Managers reported that WE disclosures were an important part of CSR reporting and had several benefits—for example, it helped to maintain companies’ reputations as good places to work, which were of interest to both investors and potential employees. However, WE reporting was at a low level, focusing predominantly on occupational health and safety performance indicators, such as the number of employee injuries per year. We suggest that organizations derive legitimacy by reporting WE disclosures within a corridor of conformance that permits a low level of reporting and a great deal of latitude regarding what topics and how topics are disclosed, provided organizations meet institutional expectations. The corridor is maintained by an institutional environment in which, from participants’ perspectives, few external stakeholders were interested in WE disclosures representing employee well-being (e.g., psychological health) and the prevailing CSR reporting standards and guidelines provide scant information about employee health.

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)0.023
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.028
GPT teacher head0.274
Teacher spread0.247 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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