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Record W2174665351 · doi:10.19030/jabr.v30i2.8433

Corporate Social Responsibility And The Quality Of Executive Compensation Disclosures

2014· article· en· W2174665351 on OpenAlex
Walid Ben‐Amar, Nadia Smaïli, Eustache Ebondo Wa Mandzila

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Applied Business Research (JABR) · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversité du Québec à MontréalUniversity of Ottawa
Fundersnot available
KeywordsExecutive compensationBusinessTransparency (behavior)AccountingCompensation (psychology)Corporate social responsibilityQuality (philosophy)Sample (material)Social responsibilityTest (biology)Public relationsCorporate governancePsychologyFinanceSocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This paper examines the relationship between corporate social responsibility and executive compensation disclosure quality. We test whether socially responsible firms disclose more transparent and detailed information about their executive compensation packages than firms that are less committed to social responsibility initiatives. Using a sample of 187 publicly listed Canadian firms, we find a positive relation between CSR and executive compensation disclosure quality. We also document a positive (negative) association between firm size (ownership concentration) and executive compensation disclosure. These findings support the conclusion that increased disclosure transparency reflects a companys social engagement towards its stakeholders.

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.

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)0.037
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0370.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.117
GPT teacher head0.366
Teacher spread0.249 · 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