Navigating the New Arctic (NNA) planning grant; Developing community frameworks for improving food security in Greenland through fermented foods (2022-2024)
Bibliographic record
Abstract
Inuit communities have sustained themselves in the Arctic for millennia through systematic knowledges about and relationships with the ecosystem of which they are a part. Because of increasing reliance on global industrial food systems there is a growing lack of access to sufficient quantities of affordable, culturally significant, and nutritious food. Arctic food insecurity stems from multiple factors, including the loss of Inuit knowledges regarding traditional food production due to negative, deficit-based stereotypes that label those foods as unsafe or disgusting. Fermented foods are among those traditional foods that have been the most criticized, even though they provide a valuable source of nutrition and health benefits. The overarching long-term goal of the planning grant project is to support the resurgence of Inuit fermented foods by generating positive, desire-based messages that recognize and value the knowledge of Indigenous fermenters, thereby improving food security in Inuit communities. The planning grant performed activities to build foundations for an Inuit-led, self-sustaining, and collaborative network in Greenland to promote Inuit fermented foods and food safety. These efforts were also intended to lead to improved scientific questions that address the future and present needs of people in the Arctic from an Indigenous perspective. Data collected for this planning grant include images, interviews, audio recordings, video recordings, and transcripts from 17 individual interviews and group discussions. These data were collected during three trips to Greenland, including two trips to South Greenland (Nanortalik, Narsarmijit, and Nunarsuaq) and one to Nuuk, spanning May 2022 to August 2024. Stakeholders included community members (elders, children, tourists, chefs, fishers, and other community members), a director of a cultural center, and a food authority supervisor. The interviews and recordings were conducted to identify key themes and to assess the desire and need for additional research and resources. The summary enclosed explains the trips, meetings and interactions, and major project findings. Key findings include how Greenlanders connect foods and land (place) together. Another finding is the expressed need for more resources and support for Greenlandic food researchers who are rooted in their communities and recognize the value of local knowledge.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".