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Record W2748475154 · doi:10.6000/1929-7092.2017.06.45

Voluntary Corporate Governance Disclosure Innovative Evidence: The Case of Jordan

2017· article· en· W2748475154 on OpenAlexvenueno aff
Ahmad Hamed Awwad Almanasir, B. Shivaraj

Bibliographic record

VenueJournal of Reviews on Global Economics · 2017
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingCorporate governanceBusinessVoluntary disclosureStock exchangeAudit committeeIndex (typography)AuditTurnoverCompliance (psychology)FinanceEconomicsManagement

Abstract

fetched live from OpenAlex

This paper aimed to assess the corporate governance voluntary disclosure level and the impact of a set of corporate governances (CG) attributes on the level to which corporate governance voluntary disclosure is conducted in Jordan. Another objective was to determine if Jordanian industrial listed corporations adhere to and disclose good CG practices voluntarily, and if they do, to determine the factors influencing such disclosure. This study employed 61 industrial listed firms for the years 2010-2014. The research developed a general voluntary CG disclosure index composing of 15 Jordanian Corporate Governance Codes and gauged the relationship via pooled OLS and regression method. The results indicated that the proposed Jordanian Corporate Governance Index (JCGI) enhanced voluntary corporate governance disclosure among Jordanian listed firms over the examined years. They also showed varying levels of CG disclosures in different scenarios; 1) it is lower in firms with higher managerial ownership and 2) higher relative to the independent directors' proportion on the board, audit firm size, and audit committee presence with institutional ownership. Evidence on the compliance level towards the CG code in Amman Stock Exchange is unique and the study is distinct as it provides a pioneering evidence of the achieved compliance level.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.062
GPT teacher head0.296
Teacher spread0.235 · 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 teacher head, 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

Citations4
Published2017
Admission routes1
Has abstractyes

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