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Record W2036875568 · doi:10.2202/1565-3404.1107

Conflicts of Interest in Publicly-Traded and Closely-Held Corporations: A Comparative and Economic Analysis

2005· article· en· W2036875568 on OpenAlexaboutno aff
Zohar Goshen

Bibliographic record

VenueTheoretical Inquiries in Law · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Law
Canadian institutionsnot available
Fundersnot available
KeywordsLoyaltyNegotiationTransaction costAdjudicationBusinessLiabilityLaw and economicsDatabase transactionEconomicsLawFinanceMarketingPolitical science

Abstract

fetched live from OpenAlex

Conflicts of interest in corporate law can be addressed by two main alternatives: a requirement of a majority of the minority vote or the imposition of duties of loyalty and fairness. A comparison of Delaware, the UK, Canada, and Israel reveals that while the conflicts of interest problem within publicly-traded corporations receives different treatment in the different jurisdictions — either a fairness rule or a majority of the minority rule — closely-held corporations receive the same treatment of an imposition of duties of loyalty and fairness. This article explains this finding, demonstrating that determining which of these rules is adopted is, in fact, a choice between liability rule protection and property rule protection. This choice depends on the total and relative transaction costs. These costs include both the negotiation costs attendant upon a property rule, as well as the adjudication costs associated with a liability rule. The sum of these costs is influenced by the efficacy of the judicial system and of extralegal mechanisms such as the market for corporate control, the capital market, and the types of investors active in the market. Because the different jurisdictions have different relative costs, due to differences in the economy and the legal systems, publicly-traded corporations are treated differently in each system. However, sometimes conflict of interest situations share the same main characteristics — as with closely-held corporations—leading to the domination of one solution, and thus the same solution is applied for closely-held corporations in the different jurisdictions.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.004
Science and technology studies0.0030.005
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.287
Teacher spread0.195 · 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 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
Published2005
Admission routes1
Has abstractyes

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