Vulnerability and adaptation on the icecap’s edge: farming communities in subarctic South Greenland
Bibliographic record
Abstract
Variability in climate and weather conditions and socio-economic shifts are dramatically altering the landscapes and livelihoods of peoples of the northern high-latitudes. Such escalating trends have distinctive consequences at the local and regional scale and the ability to adapt to such rapid changes will challenge cultural, social, and economic systems across Arctic regions. Using a cultural landscape approach, this study provides an in-depth qualitative analysis of sheep farming communities in subarctic South Greenland. Our study centers local voices and helps document shifting weather-related and other socio-economic challenges. We interview four farming households, one historian, and one local economic development expert to understand the broader context, the challenges and vulnerabilities, and the persistence of agriculture in this high north region. Our analysis reveals four overarching themes influencing the sustainability of sheep farming: (1) multi-generational farming heritage, (2) reciprocity and community support, (3) shifting weather and climate influences and farming livelihoods, and (4) farm operations and new opportunities. Findings from this study fill a gap in the literature on local and regional impacts of weather and socio-economic shifts in remote regions. Through the inclusion of local Inuit and other voices, we document how geographically dispersed subarctic communities are adapting to broader shifts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".