Taking up the plow (again)? Exploring the resurgence of First Nations farming and food production in the Canadian Prairies
Bibliographic record
Abstract
The Canadian Prairies are known as a centre of agriculture and food production, but the experiences of Indigenous peoples are rarely included in this narrative. This research investigated the current state of First Nations farming and food production (FNFFP) in Central Saskatchewan. I explored the interest, ideas, and efforts of local First Nations to build their own food systems and to use food production as a driver of community development. Empirical data were gathered through: semi-structured interviews with the “Champions” who spearhead FNFFP initiatives in the region, along with the organizations that support them; an intrinsic case study of Muskeg Lake Cree Nation’s “food forest” initiative, drawing on participant observation and semi-structured interviews; and, the use of a document review and semi-structured interviews to learn how past (twentieth century) experiences shape the sector today. FNFFP Champions (including those from Muskeg Lake) were brought together to discuss initial research findings. While the sector’s growth has been restricted due to a lack of enabling government policies and programs, and the socio-economic challenges that First Nations face in the region, a significant number of communities are investing time, energy, and ideas into FNFFP initiatives. They do so for multiple reasons, including health, food security, and land-based education. With the help of Champions and supportive organizations (both Indigenous and non-Indigenous), First Nations are innovating to build capacity, overcome barriers, and use food, and the growing of food, as a vessel for broader community development and self-determination goals.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".