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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it