Towards an Integrated Indigenous Food Security Strategy for Ontario
Bibliographic record
Abstract
Indigenous communities experience household food insecurity rates three times the national average. In Ontario, 78% of Indigenous communities are located in Northern Ontario in both remote and semi-remote contexts, where issues such as lack of year-round road access, high transportation costs, short growing seasons, and limited local agricultural lands lead to increased food costs and a lack of reliable access to nutritious food. Indigenous food insecurity ultimately stems from settler colonialism and a reliance upon a globalised food system that reinforces colonial power structures. Recent literature centres the importance of food sovereignty and self-determination, which would see Indigenous communities revitalising and taking ownership of their food systems. The COVID-19 pandemic exacerbated Indigenous food insecurity challenges. In response, various Indigenous-led initiatives emerged across Northern Ontario to address immediate needs, as well as work towards food sovereignty. This research will identify Indigenous-led food security initiatives that met community needs during COVID-19. Key informant interviews will be used to investigate factors that helped or hindered their ability to provide food to their communities, to identify new opportunities, and to determine optimal delivery models. The Ontario Ministry of Agriculture, Food, and Rural Affairs (OMAFRA; 2022) has indicated that an integrated Indigenous food security strategy for Ontario is needed. This research can assist in identifying the components of an integrated Indigenous food security strategy that meets immediate need and honours the ultimate goal of Indigenous food sovereignty, as well as identifying a clear and actionable role for the Provincial Government in supporting such a strategy.
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 | 0.003 | 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 | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".