The Theory of Auditing Economics: Evidence and Suggestions for Future Research
Bibliographic record
Abstract
Analytical auditing research provides underlying theory for the economic analysis of auditing. Despite its importance,there have been no papers that provide a comprehensive review of research conducted in this area. This monograph reviews existing analytical auditing research. I identify the assumptions and key economic forces addressed in selected papers and provide suggestions for future research by summarizing and critiquing the existing research. I develop a framework that incorporates the demand for and supply of audits and considers the economic incentives and strategic interactions of individuals or organizations involved in the audits. Using this framework, I examine three questions: Why do we need financial statement audits? What determines the assurance production and reporting? How do parties interact within an audit and in markets? This monograph intends to stimulate further analytical auditing research and help guide research using other methodologies.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".