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Record W2070106348 · doi:10.3763/jsfi.2010.0005

Mapping a corporate governance exchange: a survey of Canadian shareholder resolutions 2000–2009

2011· article· en· W2070106348 on OpenAlexaboutno aff
Taylor R. Gray

Bibliographic record

VenueJournal of Sustainable Finance & Investment · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceShareholderBusinessAccountingDivergence (linguistics)Diversity (politics)StakeholderPolitical sciencePublic relationsFinanceLaw

Abstract

fetched live from OpenAlex

The landscape of shareholder resolutions within an economy provides insight into the various perspectives as to what constitutes appropriate corporate forms and functions. This landscape arises from a community of practice and amounts to a public corporate governance exchange. Analysis of all shareholder resolutions filed with Canadian corporations from 2000 to 2009 reveals that Canada's distinctly multi-jurisdictional model of corporate governance and preponderance of block holdings serve to significantly limit the corporate governance exchange. Compounding such limitations is the tendency of large Canadian institutional shareholders to refrain from engaging in the public corporate governance exchange, thereby shrouding the behaviour of some of the most potentially influential flows of finance. Interestingly, Canadian shareholders engage in issues across 25 distinct themes relating to corporate environmental, social and/or governance performance while favouring the latter. Perhaps most consequentially, however, the public Canadian corporate governance exchange is distinctly multi-jurisdictional, thereby indicating potential regional divergence.

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)0.006
metaresearch head score (Gemma)0.023
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 score0.066
Threshold uncertainty score0.482

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.018
Science and technology studies0.0060.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.209
Teacher spread0.134 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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