Xbrl around the world: a new global financial reporting language \n
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.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".