The Influence of Affect Regulation on Professional Skepticism
Bibliographic record
Abstract
Variances in professional skepticism are a primary cause of audit deficiencies, and thus understanding how these variations happen is of keen interest to practitioners, standard-setter, and regulators so that they can better manage professional skepticism. To this end, academics have created successively more explanatory professional skepticism models. One factor known to cause variations in professional skepticism is affect, yet how it causes these variations is still not understood as the most current models cannot explain why negative affect has been found to both increase and decrease professional skepticism. The purpose of this dissertation is to build a more explanatory model of professional skepticism with respect to affect by asking and answering the question, how does affect produce variations in professional skepticism? To answer the question, I first conduct a review of professional skepticism literature and relevant affect literature to identify affect regulation as a notable theory currently excluded from professional skepticism models. Affect regulation predicts people anticipate affect as it is being generated and change their behaviour to either up- or down-regulate the affect in service of some goal. This is notable as it can explain the contradictory results with respect to negative affect and professional skepticism observed in the literature. I further investigate this with an interview study, the results of which further support the inclusion of affect regulation into a new model of professional skepticism. Then, these insights are combined to create a new model of professional skepticism. Finally, I calibrate the model with an online experiment, the results of which fail to find significant results. Taken together, the new model of professional skepticism developed herein better explains how variations in professional skepticism occur and is of use to those looking to better manage affect to optimize professional skepticism.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".