Corporate Agricultural Investment in First Nation Reserves in Canada: The Case of One Earth Farms
Bibliographic record
Abstract
In 2009, One Earth Farms (OEF) established farming operations on First Nation reserves in Saskatchewan and Alberta, Canada. The partnership that was created with First Nations was seen by some as a new model for Canadian agriculture; one that reduced agribusiness risk while enhancing the economic and social welfare of First Nation communities. Notwithstanding the purported social and economic advantages, by 2014, OEF discontinued its contracts with its First Nation partners. The failings of OEF have since been attributed to a flawed foundation, built on a culture and people with a sense of entitlement. Yet this research has found that conflicting timelines, the misalignment of goals, and failure to deliver on what was most important to First Nations are most attributable to the failing of OEF. In this paper we present important lessons learned that if considered can result in more informed and sustained partnerships between First Nations and the private agricultural sector.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.005 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".