Climate Change in Northern Quebec: Adaptation Strategies from Community-Based Research
Bibliographic record
Abstract
Arctic communities are recently reporting warmer and shorter winters, which have implications for the ice season and, consequently, on the access to local territories and resources by members of these communities. These climatic shifts are resulting in increased risks for travel during the winter season associated with less stable and thinner ice. An integrated community-based monitoring (ICBM) program was developed in Nunavik to generate adaptation tools to support safe access to land and resources and to enhance local adaptive capacity through participation in community-based monitoring activities. The Nunavik ICBM approach brings together partners (northern communities, Canadian universities, and various organizations) that have different perspectives on the issues surrounding land and resources in Nunavik. The ICBM project also brings together traditional knowledge and scientific knowledge, linking data collected through semi-structured interviews, local ethnocartographic interviews, and ice-monitoring activities with data gathered at weather stations. The partnership-based Nunavik ICBM program dealing with territory and resource access is an example of communities and scientists working together to improve our understanding of climate change impacts in the North, their importance for aboriginal people, and the ways in which an integrated, cooperative research process can develop local adaptive capacity.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".