Bibliographic record
Abstract
This book explores the challenges facing food security, sustainability, sovereignty, and supply chains in the Arctic, with a specific focus on Indigenous Peoples. Offering multidisciplinary insights and with a particular focus on populations in the European High North region, the book highlights the importance of accessible and sustainable traditional foods for the dietary needs of local and Indigenous Peoples. It focuses on foods and natural products that are unique to this region and considers how they play a significant role towards food security and sovereignty. The book captures the tremendous complexity facing populations here as they strive to maintain sustainable food systems – both subsistent and commercial – and regain sovereignty over traditional food production policies. A range of issues are explored including food contamination risks, due to increasing human activities in the region, such as mining, to changing livelihoods and gender roles in the maintenance of traditional food security and sovereignty. The book also considers processing methods that combine indigenous and traditional knowledge to convert the traditional foods, that are harvested and hunted, into local foods. This book offers a broader understanding of food security and sovereignty and will be of interest to academics, scholars and policy makers working in food studies; geography and environmental studies; agricultural studies; sociology; anthropology; political science; health studies and biology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".