Knowledge Collaboration among Tax Professionals through the Lens of a Community of Practice
Bibliographic record
Abstract
This paper presents knowledge collaboration among tax professionals in a tax-knowledge context within Malaysian accounting associations through the conceptual lens of a community of practice. Semi-structured in-depth interviews were conducted with a total of 29 tax professionals. Additionally, data were also gathered from field notes and archival data. The findings revealed that the Malaysian accounting-professional associations reflected a community of practice. Knowledge collaboration occurs among members in this community in order to attain the highest standard of technical and professional competency in tax knowledge and practice. The findings from this study complement and expand previous research on CoP, knowledge management, and collaboration. The findings suggest exploring a better strategy to implement a central repository of knowledge acquired or generated by the members within the community to support the learning lifecycle.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".