Navigating the spectrum of aggressiveness: Social dynamics and anxieties in tax planning
Bibliographic record
Abstract
This qualitative inquiry investigates how tax professionals understand aggressiveness in tax planning and how they position themselves on the spectrum of aggressiveness. Based on semi-structured interviews with 33 experienced Canadian tax professionals from top-10 accounting and law firms, we find that tax professionals understand aggressiveness through a web of inter-related considerations. These include creativity, complexity, legal ambiguity, and lucrativeness, associated with risks of tax audits , technical errors, disputes with tax authorities over legal interpretations, and reputational damage for the client, the tax professional, and their firm. These considerations and related risks are often a source of anxiety for tax professionals. Drawing on contemporary philosopher Charlie Kurth's distinction between “punishment anxiety” and “practical anxiety,” we identify an intricate interplay between these two forms of anxiety and a collective deliberation process involving clients and colleagues, each bringing their own risk-reward preferences, which shapes professionals' decisions of how aggressive they should be. The socio-affective conceptualization of aggressiveness that we propose in this study contributes to the tax literature by deepening our understanding of the elusive concept of tax aggressiveness. It also enriches the broader literature on accounting and finance professionals' emotions at work by documenting the analytical value of a nuanced understanding of anxiety. Furthermore, it advances the professional ethics literature by highlighting the moral significance of practical anxiety in professional judgment about risky, ethically sensitive issues.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".