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Record W1513941113 · doi:10.22164/isea.v6i1.62

Standalone CSR Reports: A Canadian Analysis

2012· article· en· W1513941113 on OpenAlexaboutno aff
Lois S. Mahoney

Bibliographic record

VenueIssues in Social and Environmental Accounting · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityBusinessArgument (complex analysis)AccountingIndex (typography)Strengths and weaknessesMarketingPublic relationsPolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

This research examines whether firms who issue these reports are really more socially responsible or if they are merely trying to convince stakeholders that they are. We use the CSID index as an independent evaluation of firms’ level of CSR and find that firms that issue standalone CSR reports every year do have significantly higher Total CSR, CSR Strengths and CSR Weaknesses than firms that never issue these reports. Additionally, we find that firms that issue standalone CSR reports in some years have higher Total CSR and CSR Strengths scores than firms that never issue standalone CSR reports. Our results provide support for the explanation that firms who issue standalone CSR reports do so as a signal of their superior commitment to social responsibility actions. We found minimal support for the argument that firms who issue standalone CSR reports are more profitable.

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0100.028
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2012
Admission routes1
Has abstractyes

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