Research on the Human Dimensions of Climate Change in Nunavut, Nunavik, and Nunatsiavut: A Literature Review and Gap Analysis
Bibliographic record
Abstract
Research on the human dimensions of climate change (HDCC) in the Canadian Arctic has expanded so rapidly over the past decade that we do not have a clear grasp of the current state of knowledge or research gaps. This lack of clarity has implications for duplication of climate policy and research, and it has been identified as a problem by communities, scientists, policy makers, and northern organizations. Our review of current knowledge about the HDCC in Nunavut, Nunavik, and Nunatsiavut indicates that the effects of climate change on subsistence harvesting and other land-based activities and the determinants of vulnerability and adaptation to such changes are well understood. However, the effects of climate change on health are less known. In the nascent research on this topic, studies on food security and personal safety dominate, and little peer-reviewed scholarship focuses on the business and economic sector. Published research shows a strong bias toward case studies in smaller communities, especially communities in Nunavut. Such studies have focused primarily on negative impacts of climate change, present-day vulnerabilities, and adaptive capacity, but studies proposing opportunities for adaptation intervention are beginning to emerge. While documenting the serious risks posed by climate change, they also highlight the adaptability of northern populations and the effects of economic-political stresses on vulnerability to changing climate. We note the absence of studies that examine how Northerners can benefit from new opportunities that may arise from climate change, or assess how the interaction of future climatic and socio-economic changes (specifically, resource development and enhanced shipping) will affect their experience of and response to climate change, or discuss the broader determinants of vulnerability and adaptation.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.044 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".