Rise of Sustainable Corporate Governance in Emerging Economies: Perspective of Government Auditor Capacity and Legislation
Bibliographic record
Abstract
As part of the environmental, social, and governance (ESG) ecosystem, this paper evaluates fundamental success factors that influence external auditors and relevant stakeholders to be proactive and efficacious in sustaining corporate governance practices in emerging economies. The study presents a preliminary and conceptual policy framework aimed at enhancing sustainable corporate governance, to ensure effective auditing in the public sector, by applying an extensive approach based on agency and corporate risk management theories. Applying an online qualitative technique, exploratory focus groups were held in three countries. The participants were selected by their respective Supreme Audit Institutions, based on their experience and proficiency in public sector auditing. Among the fundamental success factors identified were capacity building for auditors. Validation interviews were conducted to confirm the conceptual government auditor capacity policy framework that is presented. Executive governments, legislatures, and legislative oversight bodies can benefit greatly from the empirical segment of this study to enhance sustainable corporate governance in emerging economies and obtain greater contributions from government auditors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".