Xbrl around the world: a new global financial reporting language \n
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
In this global era, as business world looking at international level there is a need of common financial reporting language to interact financial information at a \nglobal level. Different countries follows different reporting format in order to remove diversity in reporting, XBRL is the best solution as it is transparent, reliable, \ncost saving, time saving, greater efficiency, improved accuracy etc., which will be great revolution in the accounting area in building common global reporting \nlanguage. Charles Hoffman is Known as the founder of XBRL in the 1997. XBRL is an Web-based business reporting language that is rapidly becoming an \nInternational standards for financial reporting. It holds the promise of improving the efficiency of producing, disseminating and using a compnay’s financial and \nnon-financial information. It provides cost savings, great efficiency, transparency, comparability, improved accuracy and reliability to both suppliers and users of \nfinancial data. This paper aims to study the XBRL implementation around the world i.e., USA, Canada, China, Australia and India and also their implementation \nprocess using Secondary data method.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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