Sowing a way towards revitalizing Indigenous agriculture: creating meaning from a forum discussion in Saskatchewan, Canada
Bibliographic record
Abstract
Agriculture is practiced on 3–4 million acres of First Nations reserve lands in the Saskatchewan Prairies—predominantly by non-Indigenous farmers. A confluence of factors including an increase in agricultural land holdings on reserve and greater autonomy in land management have renewed conversations on how First Nations can realize the full economic benefits and exert greater control over agricultural activities that affect the reserve land base. We hosted a Forum on Indigenous Agriculture to share current knowledge on the contemporary status of Indigenous agriculture and to co-formulate research, capacity building, and policy priorities. First Nations’ roles in agriculture are diverse and were categorized in three broad contexts: as farmers, relying on traditional Indigenous or western practice, or a synergy of both; as landlords negotiating lease agreements; and as agribusiness entrepreneurs. Five themes emerged from the forum: centring Indigenous knowledge and traditional relationships to the land, capacity building, building respectful partnerships and relationships, financing farming and equitable economies, and translating research to policy and legislation. The forum provided foundational data to inform research and capacity building to meet community-defined goals in agriculture on reserve lands and by First Nations people.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.058 | 0.017 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".