‘ Ughą jesedǔdla k’e’ - We work together : The impact of modern treaty First Nations in the Yukon working together on the implementation of the Final and Self-government Agreements
Bibliographic record
Abstract
This master’s thesis examines how modern treaty First Nations in the Yukon (MTFNYs) currently collaborate, and the potential impact of MTFNYs collaborating more on the implementation of the Final and Self-government agreements. It explores how MTFNYs, and non-Indigenous governments (Canada and Yukon) describe the implementation of the agreements; the benefits and obstacles to collaboration according to participants; and whether increased collaboration amongst MTFNYs could improve the implementation of the agreements. I argue that more collaboration would lead to increased resources for MTFNYs, an important need cited by participants for improved implementation. Collaboration would increase the power of MTFNYs, helping to offset the power imbalance between MTFNYs and non-Indigenous governments. This would improve negotiated outcomes for MTFNYs which leads to better implementation. Also, collaboration has indirect benefits such as improved relationships, increased trust and better solutions which could support improved implementation. And finally, when MTFNYs collaborate and use the court system it helps to improve implementation by balancing power between MTFNYs and non-Indigenous governments. Overall, participants saw collaboration as a useful tool to implement the agreements but saw many obstacles to collaboration. Only MTFNYs know whether the additional work of collaboration will overcome the obstacles leading to the desired results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".