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Record W4205786754 · doi:10.5539/ibr.v15n1p121

Dual Class Model and Shareholder Agreements: An Analysis of Italian Companies

2021· article· en· W4205786754 on OpenAlexvenueno aff
Anna Paola Micheli, Carmelo Intrisano, Anna Maria Calce

Bibliographic record

VenueInternational Business Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsShareholderBusinessMarket capitalizationDual (grammatical number)VotingCapitalizationControl (management)Market valueCorporate governanceFinanceEconomicsStock market

Abstract

fetched live from OpenAlex

This paper analysed the changes in ownership concentration of the Italian financial market and the recourse to dual class model and shareholder agreements by Italian listed companies in the period 2009-2020. The analysis shows that the control market did not show signs in the period that would lead to presume an increase in the contestability of our companies. The attenuation in ownership concentration, highlighted by the reduction in the value of the Shapley-Shubik index, and the increase in the average market participation did not produce an increase in the contestability of Italian listed companies since the high concentration and limited contestability of control continue to characterize their ownership structures. Findings also show less recourse by the Italian companies to the instruments of separation between ownership and control in the considered period. The reduction in the number of companies that resort to the issue of shares without voting rights and the shareholders' agreements is also reflected in the lower incidence of the capitalization of these companies compared to the market capitalization.

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)0.001
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 score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.117
GPT teacher head0.348
Teacher spread0.231 · 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
Published2021
Admission routes1
Has abstractyes

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