The fallacy of reconciliation: self-determination, self-government, and modern treaty fiscal taxation regimes
Bibliographic record
Abstract
Modern treaties are lauded as hallmarks of reconciliation between Indigenous Nations and the Crown.Integral to reconciliation are the recognition and exercise of rights of self-government and self-determination, as understood in the United Nations Declaration on the Rights of Indigenous Peoples, its enabling Canadian legislation, and the Truth and Reconciliation Commission's Reports and Calls to Action.Rather than affirming the exercise of these rights, however, fiscal taxation regimes in Modern Treaties and relevant Side Agreements actively undermine reconciliation through fiscal and taxation provisions that restrict funding for the exercise of selfgovernment and self-determination.Employing a doctrinal, qualitative and close reading analysis of publicly available data, secondary literature and jurisprudence, and guided by Indigenous critical legal theory and decolonial theory, I demonstrate that three flaws of fiscal taxation regimes in Modern Treaties are undermining these rights: (1) the imposition of colonial taxes on Indigenous citizens; (2) the limitation on the types of taxes that can be levied by Indigenous Governments; and (3) the reduction of fiscal transfers from the federal and provincial/territorial governments when own source revenue, including tax revenue, is accrued by Indigenous Governments.I employ a case study of the Nisga'a Nation to demonstrate how these three flaws function in practice, and how they may be eliminated.I argue that Indigenous constitutions and Indigenous laws incorporated therein may work to improve the functioning of fiscal taxation regimes.True nationto-nation fiscal relationships should be fostered between Indigenous, federal and provincial/territorial governments that support rather than hinder appropriate implementation of Modern Treaties, including the rights to self-government and self-determination, in order to further the objectives of reconciliation. Thank you to Professor Allison Christians and Professor Samuel Singer for your encouragement and guidance
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".