The Taxation Exemption of Canadian Indians as Governments and Individuals: How Does This Compare with Australia and New Zealand?
Bibliographic record
Abstract
This paper contributes to the emerging scholarship on the issue of Indigenous peoples and taxation, an area that has been neglected to date, through comparing the experiences in three nations. It argues that the use of legislation to exempt First Nations peoples from taxation in Canada in certain situations, and alternative approaches in New Zealand, provide models against which this nascent area can develop in Australia. The paper explores the Canadian approaches regarding Indian reserves, both exempting activities from mainstream tax regimes and accommodating taxation by Indian governments in their own right as well as the different strategy invoked in New Zealand. The more nuanced approaches to taxation of Indigenous peoples in both countries contrast with the Australian situation, where the focus in Indigenous rights has been on land rights, recognition of which has been subject to judicial inertia and political whim over the past decade. The concept of a ‘charitable’ organization is discussed as an alternative method for minimizing the tax burden: this approach has the further requirement that it must be in the public interest or the interest of an appreciable sector of the community. While charities also have limitations, they may be relied upon as a means of reducing tax liability for community benefit purposes such as economic development, health and education.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".