Breaking New Trail? First Nations and Municipal Government Cooperation in Rural Yukon
Bibliographic record
Abstract
Rural communities in the Yukon tend to be very small, most with fewer than 1,000 people, with mixed Indigenous and non-Indigenous populations. Although small, these communities face economic, social, and environmental issues similar to larger centres. These problems are complex and require a collective response from multiple governments or organizations. This research project explored the factors of inter-organizational collaboration and examined the status of cooperation between Self-Governing First Nations (SGFNs) and municipalities in rural Yukon in order to understand the factors that strengthen collaborative processes and any barriers to these processes. The project involved interviews with six key informants who are, or were, directly involved with a municipality, territorial government, or an SGFN. The research found that while most SGFNs and municipalities engage with each other, the trend is towards minimal cooperation, although relationships are improving slowly. All respondents agreed that SGFNs and municipalities in rural Yukon should collaborate more, for reasons including the need to make the best use of resources and social justice such as reconciliation. Frequently cited barriers to collaboration include a lack of human resource capacity and staff turnover. Other barriers are community histories and Indigenous and non-Indigenous relationships. The enabling factor of common understanding has some unique features in the Yukon. The region is a complex myriad of jurisdictions—territorial, First Nations, and municipal governments—with conflicting, competing, and separate mandates. However, the informants felt that a common understanding for First Nations and municipalities should be working together to benefit their entire communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".