Voluntary Corporate Governance Disclosure Innovative Evidence: The Case of Jordan
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".