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Record W2584772978 · doi:10.1057/s41267-016-0063-7

Law, finance, and the international mobility of corporate governance

2017· article· en· W2584772978 on OpenAlexafffund
Douglas J. Cumming, Igor Filatotchev, April M. Knill, David M. Reeb, Lemma W. Senbet

Bibliographic record

VenueJournal of International Business Studies · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsYork University
FundersNational University of SingaporeYork UniversityFlorida State UniversityCity, University of LondonWorld Bank Group
KeywordsCorporate governanceInternational businessCorporate financePoliticsInternational financeEconomicsInternational lawGlobal governanceAccountingFinanceBusinessPolitical scienceLawManagement

Abstract

fetched live from OpenAlex

Abstract We introduce the topic of this Special Issue on the “Role of Financial and Legal Institutions in International Governance”, with a particular emphasis on a notion of “international mobility of corporate governance”. Our discussion places the Special Issue at the intersection of law, finance, and international business, with a focus on the contexts of foreign investors and directors. Country-level legal and regulatory institutions facilitate foreign ownership, foreign directors, raising external financial capital, and international M&A activity. The interplay between the impact of foreign ownership and foreign directors on firm governance and performance depends on international differences in formal/regulatory institutions. In addition to legal conditions, informal institutions such as political connections also shape the economic value of foreign ownership and foreign directors. We highlight key papers in the literature, provide an overview of the new papers in this Special Issue, and offer suggestions for future research.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0070.006
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.041
GPT teacher head0.264
Teacher spread0.223 · 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

Citations163
Published2017
Admission routes2
Has abstractyes

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