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Record W1489870856 · doi:10.1111/irfi.12017

A Quiet Revolution in Corporate Governance: An Examination of Voluntary Best Practice Governance Policies

2013· article· en· W1489870856 on OpenAlex
Vishaal Baulkaran

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

VenueInternational Review of Finance · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsVotingCorporate governanceAccountingStock exchangeBusinessEnterprise valueTurnoverSample (material)EconomicsFinancePolitical sciencePoliticsManagementLaw

Abstract

fetched live from OpenAlex

Abstract This paper investigates the effects of several voluntary best practice corporate governance principles on firm performance and firm risk. Using a sample of Standard & Poor's/Toronto Stock Exchange Composite Index firms from 2003–2010, I show that firms with individual director election and detailed disclosure of voting results in director elections have a higher firm value or performance. Firms with independent chairman, majority voting, and detailed disclosure of voting results in director elections have lower idiosyncratic risk. In addition, the results from the panel regression show that detailed disclosure of voting results in director election leads to lower systematic and total risk.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0010.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.264
Teacher spread0.242 · 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