The Validity of Auditor Industry Specialization Measures
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 In this research note, we examine the validity of the measures of auditor industry specialization in empirical archival audit research. Industry specialist auditors are auditors who have developed a specific expertise and are therefore able to provide high quality and more efficient services to their clients. Over the years, research scholars have developed a multiplicity of measures of industry specialization (ISP). We compare 30 ISP measures and find that the use of different ISP proxies results in inconsistent classifications of auditors as specialists. Using audit fee and earnings quality models, we further show that these inconsistencies have a significant effect on the inferences drawn from the models using ISP measures. We conclude that ISP measures exhibit a low degree of internal and external construct validity. This represents an important measurement challenge for researchers and casts some doubts on the robustness of prior empirical evidence found in auditor industry specialization research.
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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 | 0.013 | 0.237 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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