CAP Forum on E‐Business: Assurance Reporting for XML‐Based Information Services: XARL (Extensible Assurance Reporting Language)*
Bibliographic record
Abstract
ABSTRACT Extensible business reporting language (XBRL) is an XML‐based method for financial reporting. XBRL was developed to provide users with an efficient and effective means of preparing and exchanging financial information over the Internet. However, like other unprotected data coded in XML, XBRL (document) files (henceforth “documents") are vulnerable to threats against their integrity. Anyone can easily create and manipulate an XBRL document without authorization. In addition, business and financial information in XBRL can be misinterpreted, or used without the organization's consent or knowledge. Extensible assurance reporting language (XARL) was developed by Boritz and No (2003) to enable assurance providers to report on the integrity of XBRL documents distributed over the Internet. Providing assurance on XBRL documents using XARL could help users and companies reduce the uncertainty about the integrity of those documents and provide users with trustworthy information that they could place warranted reliance upon. A limitation of the initial conception of XARL was its tight linkage with the XBRL document and the comparatively primitive approach to codifying the XARL taxonomy. In this paper, we have reconceptualized the idea of XARL as a stand‐alone service for providing assurance on potentially any XML‐based information being shared over the Internet. While our illustrative application in this paper continues to be XBRL‐coded financial information, the code that underlies this version of XARL is a significant revision of our earlier implementation of XARL, is compatible with the latest version of XBRL, and moves XARL into the Web services arena.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.032 |
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".