Assurance on XBRL-Related Documents: The Case of United Technologies Corporation
Bibliographic record
Abstract
The eXtensible Business Reporting Language (XBRL) was developed to provide financial information users with a standardized method to prepare, publish, and exchange business information in digital format. XBRL is being used around the world for financial reporting and government e-filings. Although there has been growing awareness about assurance issues related to the use of XBRL, current audit practices and standards fall short of providing the needed guidance for the provision of assurance on XBRL-Related Documents. In this paper, we report on a mock assurance engagement that we conducted on the XBRL-Related Documents of United Technologies Corporation's 10-Q for the third quarter of 2005 and repeated on its 10-Q for the third quarter of 2008 to identify the issues that companies and auditors might encounter if they are requested to provide assurance on XBRL-Related Documents. We describe the assurance framework applied in the mock assurance engagement, present the findings from the examination process, and discuss future research opportunities associated with XBRL documents assurance.
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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 | 0.037 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".