MANAGERIAL INCENTIVES FOR EARNINGS MANAGEMENT AMONG LISTED FIRMS: EVIDENCE FROM FIJI
Bibliographic record
Abstract
High profile corporate collapses of the past decade have undermined the integrity of financial reporting. Earnings management has been of growing concern to many academics, practitioners and regulators. Despite an enormous amount of regulation and standards governing the financial reporting process, earnings management practices are accelerating at an alarming rate in organizations today. Fiji, like many other developed countries, has had instances of financial reporting failures. One does not need to look further than the multimillion-dollar saga involving the state owned Bank, the National Bank of Fiji, which was the largest known financial scandal in the history of Fiji and the Pacific Islands. This suggests that even emerging economies like Fiji were long ago, introduced to earnings management practices. However, this has not been apparent. Most studies on managerial incentives for earnings management, have been conducted in the USA, UK, Canada, Australia and New Zealand. Very few studies took place in emerging economies like Fiji. This study uses a questionnaire-based approach to examine managerial incentives for earnings management among listed firms in Fiji, highlighting the most prevalent incentive.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".