Bibliographic record
Abstract
Canada has legislated its commitment to implementing the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP). This legislation requires Canada to reform its laws to be consistent with UNDRIP and to recognize Indigenous peoples’ right to self-determination. Little has been written on Indigenous peoples role in federal emergencies. Canada’s Emergencies Act and Emergency Management Act do not mention Indigenous peoples. As such there is no requirement for Canada to engage with, or report to, Indigenous peoples at times of emergencies in spite of the often devastating impacts on their people, territories, resources and infrastructure. This paper explores the ways that Canada can recognize First Nation jurisdiction and work with Indigenous peoples at times of emergencies. Experiences with forest fires, floods and invasion of the military provide lessons for how to reform Canada’s emergency laws. These reforms must be enacted through engagement with First Nations and with particular attention to capacity building, protocols and agreements for coordination and support. Canada’s emergency laws have an important role to play in implementing UNDRIP and the rights and title of First Nations in Canada. With so many natural disasters being caused by climate change, it is critical that First Nations play a strong role in Emergencies in their own territories.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".