Computer-Assisted Functions for Auditing XBRL-Related Documents
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
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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