School-Based Food Programming in the Northwest Territories: Working Towards More than Just Food Security
Bibliographic record
Abstract
There are an estimated 370 million culturally diverse Indigenous people worldwide. However, among this cultural diversity, there is one commonality that all Indigenous populations share; disparities across all dimensions of health indicators. Food access is one of primary indicators of health and despite this, Canada’s Indigenous population, especially in the North, remains overrepresented in household food insecurity statistics. This research aims at telling the story of one Northern community, Fort Providence, and the experiences around a school-based wild food program. It is written in the publishable paper format and is comprised of two papers. Drawing from approximately 25 weeks of ethnographic research, the first paper uses Homi Bhabha’s concept of Third Space to explain the unique way that Fort Providence youth navigate their local and global experiences. Using three tangible examples, I explain that the space where the local Dene practises interest with contemporary globalized influences creates a productive and empowering Third Space identity for youth. Drawing further on the ethnographic research, paper two gives a detailed description of the innovative land-based school programming that Deh Gah Elementary and Secondary School offers their students. I explain how the food systems in this community are integral to the overall health and vitality of the people. The six primary outcomes which emerged from engaging with community members display how the programming addresses community-wide cultural continuity and individual cultural identity. Together, these papers demonstrate how food systems are deeply embedded into the overall community health and well-being and exhibit the opportunities and positive impacts that land-based food education has for youth and communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".