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Record W3125813917

Reexamining Dual-Class Stock

2017· article· en· W3125813917 on OpenAlexaff
Vijay Govindarajan, Anup Srivastava

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVotingCommon stockShareholderBusinessCorporate lawStock (firearms)EconomicsFinanceMarket economyCorporate governancePolitical sciencePoliticsLawEngineering
DOInot available

Abstract

fetched live from OpenAlex

Snapchat’s initial public offering, which provided shares with no voting rights, is a culmination of the growing trend of dual-class shares. It contradicts the precept of one-share, one-vote that is essential for corporate democracy. Snapchat’s action caused an uproar among influential investors. In January 2017, a coalition of the world’s biggest money managers, which together control more than $17 trillion in assets, demanded a total ban on dual-class shares. We reason that the increasing prominence of dual-class stock is explained by the confluence of three economic trends: the growing importance of intangible investments, the rise of activist investors, and the decline of staggered boards and poison pills. A dual-class structure offers immunity against proxy contests initiated by short-term investors. It enables managers to ignore capital market pressures and to avoid myopic actions such as cutting research and development, which hurt companies in the long term. Thus, a dual-class structure is optimal in certain scenarios. We put forth alternatives to dual-class structure that enable managers to maintain control while retaining focus on sustainable value creation.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.022
GPT teacher head0.247
Teacher spread0.226 · 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
Published2017
Admission routes1
Has abstractyes

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