Solving the External Auditor Assignment Problem via GMRACCF
Bibliographic record
Abstract
The audit process is a critical component of any company's financial management. It aims to guarantee that the financial statements of a company are accurate, reliable, and in compliance with relevant laws and regulations. Additionally, auditing provides an opportunity for companies to pinpoint areas for improvement, which can help increase efficiency and reduce instances of financial fraud and corruption. As lots of companies with small scales choose to perform the audits externally rather than relying on an internal team, the evaluation of the performance has become a significant problem during building an auditor team. Meanwhile, efficiency is another factor to pursue. This paper formalizes this problem with the Group Multirole Assignment with the Cooperation and Conflict Factors (GMRACCF) model. Specifically, we propose a new method to evaluate the efficiency factors and build a cooperation and conflict factors (CCF) matrix by turning it into time CCFs between auditors. Therefore, with several experiments being conducted, we can know that time factors can have different impacts on the performance of the External Auditor Assignment Problem (EAAP) depending on how much emphasis has been placed on its influence.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".