<scp>XBRL</scp> for Financial Reporting: Evidence on Italian <scp>GAAP</scp> versus <scp>IFRS</scp>
Bibliographic record
Abstract
Abstract The systematic adoption of the eXtensible 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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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 | 0.096 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".