Bibliographic record
Abstract
This study documents the deterministic factors and the magnitude o f earnings management o f Nigerian firms by applying five discretionary accruals models using the cohort of 62 firms listed on the Nigerian Stock Exchange (NSE) over a period o f 2003-2012.It is observed that the Kothari et al. (2005) performance matched model provides better explanatory power to determine the magnitude of earnings management of Nigerian companies.Using this model, the study finds that the magnitude o f earnings management is 5.02 percent, on average.However, the industry-wise analyses disclose that earnings management is dominant within the manufacturing and energy sector of the Nigerian economy at 48.38 percent followed by the 41.93 percent in the consumer goods sector.The study reveals that the effectiveness of monitoring role by internal and external shareholders is insignificant in improving firm's transparency in financial reporting activities.This finding is indeed useful for Nigerian companies and policymakers to restructure the dynamics o f ownership structure in a way that can reduce the extent o f earnings management in the manufacturing, energy and consumer goods sectors in Nigeria.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".