Pulling in the Indigenous Fishery Cooperative Net: Fishing for Sustainable Livelihoods and Food Security in Garden Hill First Nation, Manitoba, Canada
Bibliographic record
Abstract
Cooperatives offer a vehicle for community development in less-developed countries as well as in economically challenged indigenous communities in developed countries. The authors engaged in a participatory community development research project involving students and faculty working with the Island Lake Opakitawek Cooperative (ILOC), a fishery cooperative in the remote community of Garden Hill First Nation (GHFN) in Manitoba, Canada. The project included four general components: (a) a sustainable livelihoods assessment; (b) a basic community food security assessment; (c) strategic business analysis; and (d) a business plan. The analysis shows that traditional foodways and livelihoods take advantage of prodigious natural capital, and that this indigenous cooperative offers potential to improve the fishers' quality of life. However, we also found that fishers' livelihoods and the ILOC were not sustainable without significant changes. Together, high operating expenses and low prices for whole fish are impoverishing fishers. Despite the poor return on investment, commercial fishing has continued for decades due to its connection with traditional hunting and fishing activities and the region's high unemployment level. Working collaboratively with the ILOC and GHFN community we have been able to reinvigorate the cooperative based on local food provision and fair trade.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".