Audit Risk Evaluation Using Data Envelopment Analysis with Ordinal Data
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
This study examines the data envelopment analysis (DEA) model for audit risk evaluation which was initially developed by Bradbury and Rouse (2002) and reinterpreted by Davutyan and Kavut (2005). Bradbury and Rouse (2002) apply the standard DEA model for audit risk factors, including judgemental (ordinal) measures. In the presence of ordinal data, efficiency analysis in DEA requires appropriate models to be applied instead of the standard DEA model. Accordingly, audit risk evaluation based on the standard DEA model is not assessed appropriately because the risk factors are qualitative and ordinal measures. Hence, we employ an appropriate DEA model to accurately evaluate audit risk in the presence of ordinal data. In light of the prior two studies, our results demonstrate the appropriateness of the ordinal DEA model.
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 | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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