Fixing Land Use Planning in the Yukon Before It Really Breaks: A Case Study of the Peel Watershed
Bibliographic record
Abstract
For eight years, the Yukon Government and four First Nation governments—the First Nation of Na-cho Nyak Dun, the Gwich’in Tribal Council, the Vuntut Gwitchin First Nation, and the Tr’ondek Hwech’in First Nation—have been working to create a land use plan for the Peel Watershed in northeast Yukon, Canada. This paper analyzes publicly available data on the decision-making process led by the Yukon Government following submission of a final recommended land use plan by the Peel Watershed Planning Commission. We argue that the Yukon Government failed to effectively reconcile different perspectives and values through the decision-making process. Using an analytical framework from the policy sciences, we contend that it is not the polarizing nature of these perspectives that has caused land use planning for the Peel region to break down; rather it is a broken decision-making process that to date has failed to secure the common interest. This failure has left many of those involved in the Peel region’s land use plan with the perception that their voices are no longer being heard in this process. We describe how these fractures occurred and present a number of recommendations that could improve the decision-making process for the Peel Watershed land use plan, with application for future such processes elsewhere in the Yukon.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".