Bibliographic record
Abstract
This chapter is aimed at examining the impact of IFRS convergence and revisions on the financial statements of companies listed in East Africa. This was achieved by determining whether firms report losses (LNEG) when they occur (timely loss recognition) or report small positive income (SPOS) or whether incomes reported exhibit variability in net income (NI) over time. Simultaneously, the chapter tests whether there is any influence of corporate governance on these three measures which are considered indicators of financial reporting quality. Four models applying GLS random effects are applied on 520 firm year observations for firms listed in Nairobi Securities Exchange (NSE) between 2005 and 2014. The result shows that a positive coefficient on frequency of large losses reported in the finding interpreted as firms with converged and revised IFRS recognize large losses (LNEG) as they occur. The findings also show a negative coefficient on small positive income (SPOS) interpreted as firms applying non-converged and revised standards manage earnings towards small positive amounts more than firms applying converged standards. The post convergence\revisions are significant for Chi, R2 and the residual which suggest that variability in net income (NI) improved in the post convergence period while corporate governance show insignificant mixed coefficient with the three indicators.
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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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".