Bibliographic record
Abstract
Global warming and climate change are important topics of debate in Greenland. This paper examines how the Tunumiit of East Greenland perceive the weather, the changing climate, and the local environment. It also discusses how their perceptions have been influenced by political debates on global warming, sustainable development, and wildlife management since the 1950s. In the past, if some animal species disappeared from a specific area, or if the weather turned bad, the Tunumiit would attribute this misfortune to human transgressions of rules of respect. Today, they often connect the increasingly unpredictable weather to their reduced access to natural resources and greater difficulties in travelling. Some hunters speak of a shift from seal hunting to cod fishing in East Greenland, although fishing is still perceived as a vulnerable source of income with low status. Nowadays, older methods of navigation and orientation coexist with such new technologies as GPS and mobile telephones. Some local hunters and villagers feel unfairly accused of increases in CO2 emissions and pollution from their motorboats and generators. Tunumiit hunting communities are facing increasing uncertainty on all levels of their existence, and their hunters are turning to the growing tourism industry—a side effect of global warming—and other coping strategies to maintain their local subsistence activities and to reinforce their own culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".