MétaCan
Menu
Back to cohort
Record W4303437248 · doi:10.7202/1092014ar

Expressive Voting and Irrational Outcomes in Corporate Elections

2022· article· en· W4303437248 on OpenAlexvenueno aff
Bryce C. Tingle

Bibliographic record

VenueMcGill Law Journal · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsVotingShareholderCorporate governanceVoting trustDisapproval votingValue (mathematics)PoliticsLaw and economicsEconomicsBusinessPolitical scienceAccountingLawFinance

Abstract

fetched live from OpenAlex

Over the past three decades, shareholders have steadily been provided with greater voting power over corporate decisions. A great academic debate has arisen about the character and outcomes of the shareholder franchise. All parties to this debate start with the assumption that shareholders will vote rationally, and generally in their economic interests. There is a large empirical literature in political science, however, that finds that where the marginal value of a vote is low, information is processed and votes cast on the basis of strongly held prejudices, tribal loyalty, mood-affiliation, and a desire to flatter the voter’s self-image. In other words, the voter behaves irrationally from the standpoint of the real-world impact of their vote. This article reviews the empirical literature around shareholder voting to show that irrational voting characterizes the corporate franchise as well. Shareholders give their voting rights almost no value and their voting patterns do not reflect the economic performance of the company. Moreover, shareholders vote in ways that contradict their economic views (measured by looking at their trading decisions), and their voting is primarily driven by empirically questionable and deliberately ineffective corporate governance practices. Fortunately, the empirical political science literature provides some direction for reforming the corporate franchise.

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)0.008
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
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 head0.029
GPT teacher head0.216
Teacher spread0.186 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMcGill Law JournalSame topicCorporate Finance and GovernanceFrench-language works237,207