Collective identity and the coalescence of an expert occupational community: The case of the Canadian tax profession
Bibliographic record
Abstract
Abstract Although the community of tax professionals is a key actor in the tax realm, its nature continues to remain elusive in many countries. Using a qualitatively driven mixed‐methods approach that integrates the insights obtained from in‐depth interviews and the results of a survey of practitioners, we examine the Canadian tax field. Although tax work has traditionally been dominated by lawyers and accountants, our study finds that a distinct expert occupation has taken shape, as evidenced by the collective identification of those working full‐time in the area. Theoretically, we show how individuals can effectively generate an occupational community through their collective identification with it and how professions that do not fit the ideal type or that are in the process of emerging may be understood. The concepts of boundary erasure and boundary emergence are introduced as variants of boundary blurring and boundary making to explain how boundaries in a professional field may be reconfigured, allowing for the emergence and informal closure of occupations. Advancing the understanding of occupational groups that have not yet embraced the professional project, our study offers insight into why some forms of expertise might not professionalize in the traditional way. Overall, the findings have implications for research on tax professionals as well as for efforts to govern their work.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.061 | 0.036 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".