Readiness for climate change adaptation in the Arctic: a case study from Nunavut, Canada
Bibliographic record
Abstract
There is limited knowledge on institutional factors constraining and enabling climate change adaptation in Arctic regions, or the overall readiness of governing bodies and communities to develop, implement, and promote adaptation. This paper examines the preparedness of different levels of government to adapt in the Canadian Arctic territory of Nunavut, drawing upon semi-structured interviews with government personnel and organizations involved in adaptation. In the Government of Nunavut, there have been notable developments around adaptation planning and examples of adaptation champions, but readiness for adaptation is challenged by a number of factors including the existence of pressing socio-economic problems, and institutional and governmental barriers. Federally, there is evidence of high-level leadership on adaptation, the creation of adaptation programs, and allocation of funds for adaptation, although the focus has been mostly on researching adaptation options as opposed to supporting actual actions or policy change. The 2016 Pan-Canadian Framework on Clean Growth and Climate Change, and increasing emphasis on climate change federally and in the Government of Nunavut, offer opportunities for advancing adaptation, but concrete steps are needed to ensure readiness is enhanced.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".