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Record W2531775601 · doi:10.5539/ibr.v9n11p201

Corporate Governance and Law Enforcement in Albanian Joint Stock Companies

2016· article· en· W2531775601 on OpenAlexvenueno aff
Amali Çipi, Enida Pulaj, Raman Ismaili

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementCorporate governanceBusinessLegislationAccountingValuation (finance)Industrial organizationFinanceLaw

Abstract

fetched live from OpenAlex

Proper implementation of CG regulations by the companies brings advantages both for companies and countries. Furthermore, the quality of legal rules determines the shape of the ownership concentration structure of the firm and, in many cases, assumes a monitoring role. In this sense for Albanian economy improving the use and enforcement of “good” CG practices will lead to higher foreign investment and soft passage towards modern economy. In this paper, we aim to analyze the evolutionary patterns of CG legal framework in Albania and, based on an application of the Delphi technique, provide development prospects considering the perceptions of a panel of Joint Stock Companies – JSCs- Chief Executive Officers –CEOs. Our essay demonstrates that CEOs expresses overall satisfaction with the CG legislation but its implementation isn’t at the required levels. Additionally, larger gaps and variations exist in areas where regulations and guidelines are less demanding or enforcement is difficult.

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)0.004
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: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.305
Teacher spread0.202 · 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
GenreOther

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

Citations1
Published2016
Admission routes1
Has abstractyes

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