Computer-Assisted Functions for Auditing XBRL-Related Documents
Bibliographic record
Abstract
ABSTRACT The increasing global adoption of XBRL and its potential to replace traditional formats for business reporting create a need for quality assurance for XBRL-tagged data. Although prior studies have addressed assurance issues on XBRL-related documents (i.e., instance documents and extension taxonomy) and related audit objectives, they primarily focus on the U.S. and, thus, may not be comprehensive enough for use in other countries. Furthermore, no prior literature discusses what and how computer-assisted audit functions can help auditors while they are performing assurance on XBRL-related documents. The main goal of this paper is to introduce computer-assisted audit functions that can be used by auditors to perform audit tasks to attain identified audit objectives. Based on professional guidelines and prior academic studies, this study introduces a set of audit objectives and related audit tasks that auditors might confront if they are asked to provide assurance on XBRL-related documents. The study then demonstrates a set of related computer-assisted audit functions for conducting the audit tasks and discuss how the identified audit objectives could be achieved using these functions.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".