Grassroots and Global Governance: Can Global–Local Linkages Foster Food System Resilience for Small Northern Canadian Communities?
Bibliographic record
Abstract
Communities in Canada’s Northwest Territories (NWT) are at the forefront of the global climate emergency. Yet, they are not passive victims; local-level programs are being implemented across the region to maintain livelihoods and promote adaptation. At the same time, there is a recent call within global governance literature to pay attention to how global policy is implemented and affecting people on the ground. Thinking about these two processes, we ask the question: (how) can global governance assist northern Indigenous communities in Canada in reaching their goals of adapting their food systems to climate change? To answer this question, we argue for a “community needs” approach when engaging in global governance literature and practice, which puts community priorities and decision-making first. As part of a collaborative research partnership, we highlight the experiences of Ka’a’gee Tu First Nation, located in Kakisa, NWT, Canada. We include their successes of engaging in global network building and the systemic roadblock of lack of formal land tenure. Moreover, we analyze potential opportunities for this community to engage with global governance instruments and continue connecting to global networks that further their goals related to climate change adaptation and food sovereignty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".