Preferences, perceptions, and veto players: explaining devolution negotiation outcomes in the Canadian territorial north
Bibliographic record
Abstract
ABSTRACT Since the early part of the 20th century, the federal government has engaged in a long and slow process of devolution in the Canadian Arctic. Although the range of powers devolved to the territorial governments has been substantial over the years, the federal government still maintains control over the single most important jurisdiction in the region, territorial lands and resources, which it controls in two of the three territories, the Northwest Territories and Nunavut. This fact is significant for territorial governments because gaining jurisdiction over their lands and resources is seen as necessary for dramatically improving the lives of residents and governments in the Canadian north. Relying on archival materials, secondary sources, and 33 elite interviews, this paper uses a rational choice framework to explain why the Yukon territorial government was able to complete a final devolution agreement relating to lands and resources in 2001 and why the governments of the Northwest Territories and Nunavut have not. It finds that the nature and distance of federal-territorial preferences, combined with government perceptions of aboriginal consent and federal perceptions of territorial capacity and maturity, explain the divergent outcomes experienced by the three territorial governments in the Canadian arctic. The following acronyms are employed: AIP: Agreement-in-Principle; DTA: Devolution Transfer Agreement; GEB: gross expenditure base; GN: Government of Nunavut; GNWT: Government of Northwest Territories; NCLA: Nunavut Land Claims Agreement; NTI: Nunavut Tunngavik Incorporated; NWT; Northwest Territories; ON: Ontario; TFF: Territorial Formula Financing; UFA: Umbrella Final Agreement; YDTA: Yukon Devolution Transfer Agreement; YTG: Yukon Territorial Government; YK: 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".