Accounting and tax compliance behaviours of ethnic and indigenous entrepreneurs: a New Zealand perspective
Bibliographic record
Abstract
The influx of immigrants in most developed nations within the English speaking world has resulted in culturally and linguistically diverse populations in Australia, Canada, New Zealand, the United Kingdom and the United States of America. Despite this, government policies within these developed nations have remained largely Anglo with little regard for the growing cultural diversity and the difficulty ethnic groups have in effectively assimilating into the broader host culture. This paper examines the existing tax policies and tax administration in New Zealand and their effect on ethnic and indigenous entrepreneurs’ accounting and tax behaviours. With sparse accounting and tax research on race and culture, there is much to be gained from an in-depth qualitative study on the tax practices and perceptions of ethnic and indigenous entrepreneurs in New Zealand. The study found that differences in tax practices and perceptions by ethnic and indigenous entrepreneurs are related to differences in their cultural values. The findings warrant further attention from accountants, academics, the business community, policy makers in terms of accounting and tax education, tax administration, tax assistance and tax regulation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".