Co-management of Traditional Foods: Opportunities and Limitations for Food Security in Northern First Nation Communities
Bibliographic record
Abstract
Traditional foods that First Nations peoples harvest or gather from the land remain critically important for achieving and sustaining food security for many communities. In Canada’s North, land claim agreements include provisions for First Nations to participate in the governance of their traditional territories, including the co-management of important traditional (wild-harvested) food species. Because such agreements only specify the broad contours of co-management governance, their actual functioning evolves out of a complex interplay among the co-managing organizations over the course of time. This paper aims to deepen our understanding of how First Nations communities can enhance food security as participants in co-management. Our study connects research on food security with research on co-management and is the first to analyze how First Nations can improve their food security by influencing decision-making that affects traditional foods through co-management arrangements. Following a succinct review of the Indigenous food security and co-management literatures, we analyze the experiences of Kluane First Nation in enhancing community food security through the co-management of its traditional territory with Yukon Government and Parks Canada, interpreting the data in light of the theories and evidences offered by research on co-management. The analysis of data collected from semi-structured interviews and from First Nations and government resources shows that, while the co-management system is imperfect, it does offer a mechanism through which First Nations can exert influence on decisions that affect their food security. The three key themes emerging from the excerpts confirm the importance of co-management as an evolutionary and long-term process, in which trust- and relationship-building are ongoing activities that are fundamental to beneficial collaboration involving the sharing of information and power. The analysis also highlights the role of context, or situational factors, in facilitating or hindering collaboration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".