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Record W2981027213 · doi:10.22495/cocv17i1art7

Corporate governance: An analysis of the relationship between quality and cost

2019· article· en· W2981027213 on OpenAlexaffabout
Sylvie Berthelot, Michel Coulmont, Yves Levant

Bibliographic record

VenueCorporate Ownership and Control · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCorporate governanceGlobeExecutive compensationAccountingBusinessQuality (philosophy)Best practiceCompensation (psychology)Index (typography)FinanceEconomicsManagementPsychology

Abstract

fetched live from OpenAlex

The purpose of this study is to analyse linkages between the quality and cost of Canadian firms’ governance practices. With this in mind, the study relates the compensation of chief executive officers (CEOs) and non-executive directors to best governance practice index developed by The Globe and Mail. We collected data for the years 2013, 2014 and 2015, constituting 602 observations from all the Canadian companies included in The Globe and Mail corporate governance ratings for which financial information was available on the Research Insight database. We examined the relationship between the quality and cost of Canadian firms’ governance practices with a regression model. The analyses results tend to indicate some relationship between CEO and non-executive director compensation and the quality of governance practices. However, firm size appears to the determining explanatory factor. The study results also indicate that some activity sectors seem to have better governance practices than others

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)0.002
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 score0.681
Threshold uncertainty score0.642

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.092
GPT teacher head0.262
Teacher spread0.170 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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