Cataloging the Marketplace of Assurance Services
Bibliographic record
Abstract
SUMMARY We identify 33 areas where assurance services other than financial statement audits are currently offered or are emerging and conduct an extensive web search to document the contextual features of the services in each area. Using a framework for the expansion of assurance services, we analyze these features by asking: (1) Does the subject matter relate to financial information or controls? (2) Are criteria available to evaluate the subject matter? The answers allow us to categorize each area based on whether it represents an expansion opportunity to a traditional, but hypothetical, CPA firm. We then compare our expectations against observed areas where real-world firms have a presence. Finally, we report on two roundtables with senior assurance leaders to validate our findings and enhance our understanding of what is needed for each area to become, or continue to be, well-positioned for expansion by CPA firms.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".