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Record W3136256108 · doi:10.60082/2817-5069.3632

Two Stories About Shareholders

2021· article· en· W3136256108 on OpenAlexaffvenue
Bryce C. Tingle

Bibliographic record

VenueOsgoode Hall law journal · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsShareholderCorporate governanceShareholder resolutionIncentiveCorporate lawBusinessAccountingLaw and economicsAgency costEconomicsFinanceMarket economy

Abstract

fetched live from OpenAlex

Corporate law contains two contradictory stories about the role of shareholders. In one, the shareholders are a useful countervailing force against the self-interested behaviour of corporate agents. In the other, shareholders lack the motivation, information, and proper incentives to contribute to the good governance of business corporations. Both stories are true on occasion, but is one generally more true than the other? Currently, developments in corporate and securities law are predicated on the idea that shareholders are, generally, a positive force in corporate governance. This seems to be a corollary of agency cost theory, the dominant paradigm for understanding the relationships between corporate actors. This article reviews the body of empirical research on the outcomes of the various forms of shareholder activism. Proposals, proxy campaigns, and takeovers represent the most impactful and costly forms of shareholder engagement with corporations. As it happens, the empirical evidence does tend to strongly support one of the two stories about the role of shareholders, but it is not the one currently dominating law reform efforts. If the character of shareholder interventions generally supports the story that shareholders lack the proper incentives and information to contribute to positive business outcomes, then much about the current regulatory scene needs to be re-evaluated.

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)0.009
metaresearch head score (Gemma)0.025
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.023
Scholarly communication0.0150.025
Open science0.0010.007
Research integrity0.0080.013
Insufficient payload (model declined to judge)0.0080.001

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 head0.030
GPT teacher head0.236
Teacher spread0.206 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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