“Doing Good Field Research”: Assessing the Quality of Audit Field Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.540 | 0.617 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 it