A Vetting Protocol for the Analytical Procedures Platform for the AP-Phase of PCAOB Audits
Bibliographic record
Abstract
Study Context AS5[2017], issued by the Public Company Accounting Oversight Board, requires the use of Analytical Procedures [AP] at the Planning and Substantive Phases of Assurance Audits of firms traded on active exchanges. Logically, an aspect of this requirement is satisfied by using a Panel of the Client’s data at the Planning Phase to forecast the Client’s YE-closing values and then at the Substantive Phase to dispose the directional difference between the: [Actual Client’s YE-value and the AP-Forecasted YE-value]—the Disposition Phase. Research Focus To date, neither the PCAOB nor the AICPA have suggested a pilot-test paradigm to vet the AP-forecasting Protocol under consideration. To address this lacuna, we detail an AP: Decision Support System [AP:DSS] that offers to the Audit InCharge a two-stage pre-analysis AP-vetting [Pilot-Test] platform that employs False Negative [FN] and False Positive [FP] Profilers. In inferential analyses, the FP-Risk is usually benchmarked using the FN-Risk. Deliverables A comprehensive AP-vetting model is offered and illustrated using: (i) a preliminary estimator of a reasonable sample size, (ii) two Standard Forecasting Models: The Excel versions of the OLS Linear Two-parameter and the Moving Average Models, and (iii) a Benchmarking protocol. Unique in this AP:DSS vetting protocol is that the FP-risk is contexted by the FN-risk from the independent benchmark domain. This duality enhances the inferential impact of the vetting protocol as it uses separate variable sets. The AP:DSS is available at no cost as an e-Download.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".