Measuring the Effectiveness of National Enforcers in the IFRS Context: A Proactive Approach
Bibliographic record
Abstract
It is widely acknowledged that an effective enforcement system represents a crucial element to achieve significant improvements in financial reporting through the adoption of high-quality accounting standards. Indeed, the quality of financial reporting is considerably influenced not only by the standards to be adopted but also by their actual implementation, and consequently by enforcement mechanisms.The topic has generated considerable interest among scholars, who devoted their attention to developing different measures of the quality of the enforcement system. Building upon this literature, this paper aims at exploring the accounting enforcement system and focuses on controls over financial reporting considering two levels, namely the auditing activity and the controls performed by national enforcers.This paper extends the prior literature by proposing a dynamic measure of the accounting enforcement system capturing controls at those two levels. More specifically, the index here proposed focuses on the quality of the accounting enforcement operated by national enforcers in terms of proactivity, intended as the national enforcers’ capability to detect problems not highlighted in the auditors’ opinions, thus shifting the focus from an input to an output perspective. Indeed, the activities of auditors and national enforcers are strictly connected, given that the auditors’ opinion is the first public output of accounting controls and that is normally one of the bases for further investigation by national enforcers. An illustrative empirical analysis is carried out on the German and the Italian contexts to show the potential of the index for enforcement studies.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".