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Record W2918483578 · doi:10.5539/ijef.v11n3p69

The Impact of Audit Committees Controls Commitment on Strengthening Corporate Governance: Evidence from Jordan

2019· article· en· W2918483578 on OpenAlexvenueno aff
Fares Alsufy

Bibliographic record

VenueInternational Journal of Economics and Finance · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAccountingAudit committeeAuditBusinessStock exchangeTest (biology)Work (physics)Subject matterFinancePsychologyEngineering

Abstract

fetched live from OpenAlex

This study aims to determine the extent to which the Boards of Directors of the industrial Jordanian Companies listed on Amman Stock Exchange (ASE) comply with the controls of composing audit committees, their working mechanisms, and the impact on the corporate governance. To achieve the objectives of this study, (155) questionnaires were developed and distributed to the staff members relevant to the subject matter of the study. Out of distributed questionnaire, (144) responded questionnaires only were collected from respondents. The number of questionnaires analyzed was (135) and a T-test has been used to test the hypotheses. The results of the study showed that there is a statistically significant correlation on the existence of the commitment of the Boards of Directors of the Jordanian Listed Companies to the disciplines of audit committees’ formation and their mechanisms of work. The results also demonstrated the existence of impact of this commitment on the governance of these companies. The commitments to these controls and their work mechanisms have been developed to enhance corporate governance in Jordanian companies.

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)0.007
metaresearch head score (Gemma)0.015
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 score0.008
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.237
Teacher spread0.218 · 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
Published2019
Admission routes1
Has abstractyes

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