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Record W3009705738 · doi:10.22495/cocv17i3art1

The effectiveness of governance mechanisms in emerging markets: A review

2020· review· en· W3009705738 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCorporate Ownership and Control · 2020
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCorporate governanceEmerging marketsBusinessExternal auditorOrder (exchange)Independence (probability theory)DividendMechanism (biology)AuditIndustrial organizationAccountingInternal auditFinance

Abstract

fetched live from OpenAlex

Corporate governance has advanced hugely in the last two decades and many governance best practices have emerged that focuses on measures companies should take in order to improve their governance. These suggested mechanisms are effective in developed markets because they are a remedy for problems that occur in those markets. But are these mechanisms also effective in emerging markets? By reviewing the literature, this paper critically discusses and compares the effectiveness of governance mechanisms (both internal and external) in emerging and developed markets and finds that while the classic mechanisms such as board structure and independence are not effective in emerging markets, there exist some alternative mechanisms such as external audit or dividend policy that are more effective.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.239
Teacher spread0.211 · 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