Bibliographic record
Abstract
I am greatly indebted to Steve Salterio and Alan Webb for their guidance and valuable advice on this proposal. I acknowledge the Canadian Academic Accounting Association for providing field research private communication and Shari Mann for helping me to obtain audit interviewees. I also thank Greg Berberich, Efrim Boritz, Carla Carnaghan, Sally Gunz, Natalia Kotchetova, Thomas Kozloski, Morley Lemon, Bill Wright, and two Previous field research suggests that there is an increasing need for auditors to rely more extensively on enquiry based evidence. This study investigates how using various theory- and practice-based based interventions can lead to a more rigorous enquiry process. Based on psychology theory on planning and on goal setting, as well as empirical findings in the auditing literature, I propose that both simple instructions to plan and assigning a specific enquiry goal could improve the rigor of auditors ’ enquiry process. A 2x2 plus one between-subjects experiment will be conducted to examine auditors ’ performance in an enquiry task. Auditors ’ performance will be measured by variables reflecting the level of activities, the nature of interview questions, and the propriety of organizing, analyzing and coordinating activities in enquiry. This study also extends prior field research by developing and testing a checklist that could be applied to improve the quality of auditors ’ enquiry process. 1 I.
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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 | 0.008 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.410 | 0.241 |
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".