Bibliographic record
Abstract
eXtensible Business Reporting Language (XBRL) is a language for the electronic communication of business and financial data which is revolutionizing business reporting around the world. It is a tool to bridge potential language barriers and unify financial reporting. This has appeal to foreign investors, among others, who can rely on information in XBRL-tagged financial reports to make investment decisions without having to translate financial statements from local language. In 2008, Israel required most public companies to adopt International Financial Reporting Standards (IFRS) for financial reporting and to use XBRL-tagged reporting format, as part of an aggressive effort to make its capital markets more transparent and attractive for foreign investors. In this paper, we study all Israeli public companies and analyze the accuracy and reliability of their XBRL-tagged financial statements that are available on MAGNA, the Israel Securities Authority's electronic system. We describe the process by which the XBRL-based data were collected and reported. We document, categorize, and analyze deficiencies in the XBRL-tagged filings, and inconsistencies between them and the Hebrew-based annual reports. We observe pervasive data entry errors resulting in inaccurate XBRL-generated financial reports, which went undetected for over one year. Further, first year XBRL reporting (in conjunction with IFRS adoption) did not increase foreign investment in the Israeli capital markets. This analysis allows us to better understand the benefits and challenges of the adoption of XBRL.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".