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Record W3122602886

Channels of Convergence: Investor Engagement and Interlocked Directorates

2009· article· en· W3122602886 on OpenAlexaboutno aff
Taylor R. Gray

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceConvergence (economics)Divergence (linguistics)Institutional investorDistribution (mathematics)BusinessPipeline (software)Industrial organizationSample (material)Tacit knowledgePoliticsPopulationAccountingEconomicsPolitical scienceFinanceSociologyKnowledge managementEngineeringLawEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

In a setting of globalized financial capitalism an issue which has received little attention to date is not whether national models of corporate governance are converging or diverging but rather the channels by which market and regulatory forces interact to promote such convergence or divergence. I herein propose the institutional investor/interlocked directorate pipeline as one such channel of potential convergence. In theory, institutional investors impart tacit knowledge to a select group of corporations by means of engagement activities; such knowledge and influence can subsequently be distributed to a wider corporate population by means of interlocked directorates originating from the engaged corporations. An analysis of the distribution of these pipelines across Canada demonstrates that such pipelines can penetrate geopolitical and industrial boundaries and interact with regulatory forces in shaping models of corporate governance. The wide distribution of such pipelines effectively transforms tacit knowledge originally conveyed by institutional investors to a geographically limited sample of corporations into explicit knowledge accessible across the country.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2009
Admission routes1
Has abstractyes

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