Incorporating Context into the Study of Judgment and Expertise in Public Accounting
Bibliographic record
Abstract
This paper argues for context‐sensitive research in the public accounting setting and examines numerous issues that need to be addressed in doing such research. The issues include research objectives, theory, design and method. Context is significant in shaping auditors' expertise, and a major reason for studying public accountants' (usually auditors') judgment is that their judgment therefore differs from general human judgment or the judgment of other experts. Contextual factors such as incentives, time pressure, professional standards, decision aids and interpersonal relationships make the public accounting setting distinctive and interesting. Research that examines contextual influences on judgment is valuable in understanding this setting. Context‐sensitive research depends on theory about the context, as well as on careful pilot testing and other procedures to define and incorporate context. Field research using experiential questionnaires is one useful way to get at contextual variables and assess their effects on judgment. Using such questionnaires raises various questions about research design, participants and data analysis. This paper outlines answers to those questions as suggestions about creating high‐quality research.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".