<scp>XBRL</scp> for Financial Reporting: Evidence on Italian <scp>GAAP</scp> versus <scp>IFRS</scp>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The systematic adoption of the eX tensible Business Reporting Language (XBRL) for financial reporting represents a great challenge. Worldwide, a large number of regulators are making an effort to promote the adoption of this standard to simplify and enhance the communication of financial information. This requires the definition of well‐structured taxonomies that can standardize and accommodate the content of financial reports prepared by firms. This study aims to analyze the regulator‐led adoption of XBRL for financial reporting. It examines the XBRL taxonomies used by Italian firms to reflect their financial reporting under rule‐based Italian GAAP and principles‐based International Financial Reporting Standards (IFRS). We compare the alignment of the Italian GAAP taxonomy and the IFRS taxonomy with Italian companies' financial statements and find two different levels of fit. The results offer useful insights for regulators and policy makers in prescribing or establishing appropriate taxonomies. We illustrate the potential impacts of the different taxonomies on the quality of financial reporting in terms of comparability and potential loss of information.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it