Differently Trained Practitioners’ Approaches to Tax Work: Divergence, Convergence, and Further Insights for the Profession
Bibliographic record
Abstract
In many countries, tax practitioners represent a diverse set of professionals with different backgrounds and training. Yet, because there is limited empirical research directly comparing them, little is known about how differently trained practitioners (for example, accountants and lawyers) approach tax work. Employing mixed methods in a qualitatively driven manner, we first draw on in-depth interviews with 38 mostly senior-level practitioners. From these interviews, we inductively identify four dimensions—basic orientation, basic mindset, broader sensitivity, and process orientation—along which tax professionals are seen to differ, as well as an overall philosophy that appears to guide how they approach tax work. On the basis of these findings, we conduct a second study, an open-ended response survey, that allows us to refine and substantiate these dimensions and the philosophies identified. In a third study, we use a survey to quantitatively triangulate the dimensions and philosophies identified through our qualitative data, and to delve more deeply into the extent to which practitioners may evolve and whether there is any convergence in the approaches of differently trained professionals over time. Our three studies contribute to a growing body of literature examining tax professionals and their work by advancing the understanding of how individuals typically differ in their overall approaches to tax work, by offering insight into the comparative growth in competence, and by offering a possible explanation for why some groups of professionals may be more actively involved than others in tax planning. Our findings have implications for professional associations and others who oversee the competence and quality of work of tax practitioners, as well as educational institutions and others who are charged with their training.
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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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".