Reconciling Traditional Knowledge, Food Security, and Climate Change: Experience From Old Crow, YT, Canada
Bibliographic record
Abstract
Reconciling Traditional Knowledge, Food Security, and Climate Change:Experience From Old Crow, YT, Canada Vasiliki Douglas, Hing Man Chan, Sonia Wesche, Cindy Dickson, Norma Kassi, Lorraine Netro, and Megan Williams What Is the Purpose of This Study? • To determine community food security concerns in Old Crow, Yukon, Canada and to develop a community consensus on viable adaptation planning to address food security concerns. What Is the Problem? • Old Crow is a remote, aboriginal community in Arctic Canada that is highly dependent on both traditionally harvested foods and expensive market foods for sustenance, leading to experience a high degree of food insecurity. • Environmental, cultural, and economic change is altering the relationship between residents of Old Crow and their food supply. What Are the Findings? • The population of Old Crow is deeply concerned about its youth and their ability to participate in traditional culture. • A secure food supply is closely dependent on the ability to responsibly manage and harvest traditional food species, and alter the balance between them, if necessary. • Loss of traditional knowledge and culture is widely viewed as a serious threat to traditional resource management. • Market foods are a possible substitution, as is agriculture, but must be managed in conjunction with the traditional food supply. Who Should Care Most? • Members of remote communities facing a high degree of food insecurity face similar issues. • Traditional communities with strong food traditions are facing increasing social pressure to adopt mainstream diets based on market foods. They face issues of cultural food security (if not physical food security) similar to those facing Old Crow. Recommendations for Action • Communities’ options for addressing their food security and cultural security issues may be explored with the assistance of researchers, leading to recommendations for adaptation planning in close collaboration with the community • Actual adaptations must be driven from the community level, and require a high degree of community consensus for success. [End Page 3] Vasiliki Douglas University of Northern British Columbia, School of Health Sciences; Hing Man Chan University of Ottawa, Department of Biology; Sonia Wesche University of Ottawa, Department of Geography; Cindy Dickson Council of Yukon First Nations; Norma Kassi Arctic Institute of Community Based Research Lorraine Netro Vuntut Gwitchin Government Old Crow Megan Williams Vuntut Gwitchin Government Old Crow Copyright © 2014 The Johns Hopkins University Press
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".