Successful First Nations Policy Development: Delivering Sustainability, Accountability, and Innovation
Bibliographic record
Abstract
There is a profound need for a process that will afford Aboriginal peoples the opportunity to restructure existing governmental institutions and to participate as partners in the Canadian federation on terms they freely accept. This conclusion of the Royal Commission of Aboriginal Peoples (RCAP) (1996, 244) accurately identifies a central challenge for the Assembly of First Nations (AFN). The AFN, as the political representative for First Nations governments throughout Canada, has amassed a great deal of experience in dealing with the Government of Canada. Indeed, it is our perspective that a critical determinant of a successful outcome for the full range of engagement—from senior government-to-government negotiations to policy development and singular program considerations—lies in the initial process design. This paper will provide a general overview of examples of interaction between First Nations and Canadian governments, as well as Indigenous peoples and state governments in other parts of the world. From these examples, both situations to avoid and best practices emerge. Based on this information and direction received from First Nations by way of our assemblies and policy forums, the AFN has designed a First Nations policy development model. This paper presents the First Nations policy development model and fully describes its elements, considerations, and operating principles. We also provide examples of the utility of the model guiding the engagement of the AFN in critical intergovernmental fora, as well as on specific project initiatives currently underway with the Government of Canada.
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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 | 0.054 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".