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Record W183528721

Valuation, operating performance, expenditure decisions and management ownership of listed Canadian dual-class firms

2006· dissertation· en· W183528721 on OpenAlexaboutno aff
Lei Fang

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2006
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsVotingVoting trustBusinessCash flowCommon stockCapital structureEquity (law)Financial economicsEconomicsActuarial scienceFinanceMonetary economicsDisapproval voting
DOInot available

Abstract

fetched live from OpenAlex

Dual-class capital structures, which are characterized by holders of one class of common stock having greater voting rights than holders of another class, are relatively common among Canadian corporations. The cash flow ownership and voting rights ownership of M&D (management and directors) diverge substantially for many Canadian firms with dual-class equity structures, and M&D voting rights ownership generally exceeds cash flow ownership. The relation between firm value and M&D voting ownership is negative and convex. In contrast, the relation between firm value and M&D cash flow ownership is indeterminate, which corresponds with the notion that the primary motive for dual-class equity structures is to ensure voting control. The relation between capital expenditures and M&D voting ownership is negative and convex with increasing M&D voting ownership. The relations between measures of operating performance (such as sales growth, net profit margin and return on equity) or combined R&D and advertising expenditures relative to assets and sales with either M&D cash flow or voting ownership also are indeterminate.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.251
Teacher spread0.216 · 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 designObservational
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
Published2006
Admission routes1
Has abstractyes

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