Examining Local Food Procurement, Adaptive Capacities and Resilience to Environmental Change in Fort Providence, Northwest Territories
Bibliographic record
Abstract
By exploring localized adaptation strategies for climate change, this paper aims to provide a deeper understanding of local perspectives and efforts regarding food procurement in Fort Providence, Northwest Territories (NT). The benefits and risks associated with engaging in local food procurement activities are key topics explored. Strategies to manage food insecurity and local approaches to encourage food procurement are also considered. This study was informed by Indigenous methodologies, which guided all aspects of this research. While the researchers have collaborated with community members since 2010, evidence for this study was collected during two field seasons in the spring and fall of 2018, using semi-structured interviews with Elders, land-users, and knowledgeable community members. Findings support decentralized policy developments which focus on the integration of local voices into decision-making processes and program implementation. Food policies must reflect the needs of residents at localized levels and the distinct socio-cultural and economic barriers to procuring food, and they must encourage overall community resilience and adaptive capacities to climate-related change. This research supports regional and national efforts to reduce food insecurity across northern Canada by documenting traditional knowledge concerning climate change and local food practices in Fort Providence.
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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 | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".