The Impact of the Use of Artificial Intelligence on the Development of External Audit Efficiency in Jordanian Mining and Extractive Corporations
Bibliographic record
Abstract
This study aims to identify the impact of the use of artificial intelligence on the development of external audit efficiency in Jordanian public shareholding and mining and extractive companies. The sample of the study consisted of (56) external auditors in (13) Jordanian mining and extractive corporations. The descriptive approach and analytical approach were also used for its occasion in achieving the objectives of the study. The data was processed statistically using arithmetic averages and multiple regression analysis.The study found a statistical impact of artificial intelligence on the development of external audit efficiency in Jordanian mining and extractive corporations, and the existence of a statistical impact of artificial intelligence, represented in: (Planning, carrying out control tests and basic tests of operations, carrying out analytical procedures and detailed tests of balances, auditing subsequent events and future commitments prior to the issuance of the auditor's report) in improving all dimensions of governance (effective governance framework, disclosure and transparency, shareholder equality, responsibilities of the Board of Directors, role of stakeholders) in mining companies and extraction companies in Jordanian Public Shareholding.In light of the results of the study, it recommended that the total reliance on artificial intelligence be made easier for auditors, which has a high positive impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".