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Record W4283765051 · doi:10.1080/00014788.2022.2071199

Corporate social responsibility disclosure: a topic-based approach

2022· article· en· W4283765051 on OpenAlexaff
Katrin Hummel, Stéphanie Mittelbach‐Hörmanseder, Charles H. Cho, Dirk Matten

Bibliographic record

VenueAccounting and Business Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork University
Fundersnot available
KeywordsCorporate social responsibilityThematic analysisAccountingSample (material)Convergence (economics)BusinessThematic mapEconomicsPublic relationsPolitical scienceSociologyQualitative researchEconomic growthSocial scienceGeography

Abstract

fetched live from OpenAlex

In this study, we investigate the potential differences in topic-specific corporate social responsibility (CSR) disclosure between companies located in liberal market economies (LMEs) and coordinated market economies (CMEs). We also examine the potential convergence of the reporting practices that characterise these two economies over time. We analyse a sample of 5,939 CSR reports issued by European and U.S. firms over 2008–2019. We use textual analysis to examine how explicitly such reports address specific CSR topics. Following Matten and Moon (2008), we focus on three thematic areas: ‘human resources’, ‘environmental protection’, and ‘society at large’. Each area comprises three distinct topics. Our results show that companies operating in LMEs report more explicitly on these thematic areas, with one exception: those operating in CMEs report more explicitly on parental policies. Additionally, the reporting practices of companies operating in these two types of economies converge for most of the topics under study. For the disclosure of parental leave policies, biodiversity, and waste, no distinct trend is observable.

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)0.012
metaresearch head score (Gemma)0.004
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 score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.130
GPT teacher head0.333
Teacher spread0.204 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

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