Encounters Between Wolves, Humans, and Their Dogs in West and North Greenland
Bibliographic record
Abstract
This study investigates encounters between sled dogs (Canis lupus familiaris), humans, and Arctic wolves (Canis lupus arctos) in West and North Greenland. Using qualitative data from extensive interviews conducted in North Greenlandic, Polar Inuit, and Kalaallisut dialects, as well as a comprehensive review of relevant literature, this research aims to elucidate the spatial, temporal, and situational contexts of these interactions. Early accounts from European and American explorers, and observations made by local residents, complemented by Indigenous Kalaallit and Inughuit oral traditions, reveal the adaptive behaviors of wolves and their significant effects on human activities. Evidence of wolves in Greenland dates back to Saqqaq Culture (2400–1400 BC) and the Norse settlements (985–1450 AD) and continues into contemporary times. Greenlandic myths depict wolves as both feared and respected creatures, highlighting their profound cultural significance. Recent increases in wolf sightings near settlements in Avanersuaq have raised concerns about their impact on local fauna and human safety, prompting wildlife management measures. The study finds that Arctic wolves, humans, and sled dogs have interacted across various regions of North and West Greenland, with these interactions affecting both ecological dynamics and human practices. This research underscores the complex and evolving relationships between sled dogs, humans, and Arctic wolves, advocating for balanced conservation strategies that integrate scientific research with traditional knowledge. The findings contribute to the broader discourse on wildlife management in extreme environments, providing insights into the resilience of wolves and their enduring influence on human communities in the High Arctic and West Greenland.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".