Influence factors analysis of online auditing performance assessment based on AHP and GIA
Bibliographic record
Abstract
An influence factors analysis method for online auditing performance assessment based on AHP (analytic hierarchy process) and GIA (grey incidence analysis) is proposed in this paper according to the requirements of online auditing performance assessments. In this method, a grey incidence model is developed to analyze the influence factors of online auditing performance based on the characteristics of online auditing. Then, the AHP is used to compute the weights of each assessment criterion of online auditing, and the performance of online auditing are calculated. Finally, representing the performance assessment results computed by AHP and values of each assessment criterion are set as two sequences, and GIA is used to analyze the importance degree of influence factors of online auditing performance quantitatively. The results of this study provide useful decision information to implement online auditing projects in China. Additionally, an effective method for analyzing the importance degree of influence factors of online auditing performance quantitatively is provided in this study.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".