“Doing Good Field Research”: Assessing the Quality of Audit Field Research
Bibliographic record
Abstract
SUMMARY Field research is increasingly being employed by audit researchers around the world. However, given that many doctoral programs, especially in North America, devote little or no time to this method, understanding what constitutes good auditing field research is problematic for many editors and reviewers. Hence, the goal of this article is simple: to provide editors and reviewers with a set of suggestions/guidelines that can be employed to assess the quality of auditing field research as field research. In addition, this article might be helpful to those audit researchers who are teaching themselves field research methods to calibrate their understanding of rigorous and trustworthy field-based research methods, as well as for doctoral students and accounting departments interested in expanding their scope of course offerings. To achieve this goal we pose and answer ten questions about field research quality illustrating our responses with best practices observed in currently published or forthcoming papers. We also identify various methodological resources that will assist editors, reviewers, and authors in developing a greater appreciation for and an ability to evaluate qualitative auditing 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 | 0.626 | 0.881 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".