Harvest Programs in First Nations of Subarctic Canada: The Benefits Go Beyond Addressing Food Security and Environmental Sustainability Issues
Bibliographic record
Abstract
By breaking down barriers that impacted the ability of subarctic First Nations people to harvest waterfowl, the Sharing-the-Harvest program provided a safe, nutritious, and culturally appropriate food (i.e., geese) to James Bay Cree communities while also helping to protect the environment by harvesting overabundant geese. However, the impacts extend beyond those described above. Thus, the objectives of the present paper are twofold: to document the food sharing networks of the Sharing-the-Harvest program; and to examine the benefits associated with the harvest program beyond food security and environmental sustainability issues, as revealed through semi-directed interviews. In the regional initiative, harvested geese were shared with all James Bay communities; sharing is an important part of Cree culture. Where detailed information was collected, the goose-sharing network reached 76% of the homes in one of the communities. Likewise, in the local initiative, the goose-sharing network had a 76% coverage rate of the homes in the community. Although decreasing food insecurity was an important focus of the harvest-sharing programs, there were other benefits, from an Indigenous perspective, of being on the land, as identified by the Cree harvesters through semi-directed interviews (e.g., the transmission of Indigenous knowledge, the strengthening of social networks, and the feeling of wellness while out on-the-land). Thus, by participating in the on-the-land harvest programs, the Cree gained benefits beyond those solely related to strengthening food security and contributing in part to environmental sustainability. The Sharing-the-Harvest protocol has the potential to be adapted and employed by other Indigenous (or marginalized) groups worldwide, to help improve health and wellness, while, also protecting the environment from overabundant and/or invasive species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".