Experience Exceeds Awareness of Anthropogenic Climate Change in Greenland
Bibliographic record
Abstract
Abstract Greenland’s residents and Ice Sheet are both exposed to pronounced Arctic warming. Although Greenland is a hub for climate science, the climate perceptions of Greenland’s predominantly Indigenous population remained unstudied through the past decade of multi-national climate opinion polls. Conducting two original nationally representative surveys, here we show that Greenlanders are more likely than residents of top oil-producing Arctic countries to perceive that climate change is happening, and about twice as likely to have personally experienced its effects. However, half are unaware that climate change is human-caused, and those who are most affected appear to be least aware. An Inuit cultural dimension, indicated by the intertwined social ecological factors of Indigenous identity, subsistence occupation, village community scale and no post-elementary education, proves to be the main positive predictor of climate change experience but also the strongest negative predictor of awareness of human-caused climate change. Despite Greenland’s centrality to climate research, we uncover a widespread gap between the scientific consensus and coastal Kalaallit views of climate change, particularly among the country’s youth, fishers, and hunters. This science-society gulf has immediate and prospective implications for local climate adaptation, climate science communication and knowledge exchange between generations, institutions, and communities.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".