Co-management institutions, knowledge, and learning: Adapting to change in the Arctic
Bibliographic record
Abstract
How vulnerable are Arctic Indigenous peoples to climate change? What are their relevant adaptations, and what are the prospects for increasing their ability to deal with further change? The Intergovernmental Panel on Climate Change makes little mention of Indigenous peoples, and then only as victims of changes beyond their control. This view of Indigenous peoples as passive and helpless needs to be challenged. Indigenous peoples, including the Canadian Inuit, are keen observers of environmental change and have lessons to offer about how to adapt, a view consistent with the Inuit self-image of being creative and adaptable. There are three sources of adaptations to impacts of climate change: 1) Indigenous cultural adaptations to the variability of the Arctic environment, discussed here in the context of the communities of Sachs Harbour and Arctic Bay; 2) short-term adjustments (coping strategies) that are beginning to appear in recent years in response to climate change; and 3) new adaptive responses that may become available through new institutional processes such as co-management. Institutions are related to knowledge development and social learning that can help increase adaptive capacity and reduce vulnerability. Two co-management institutions that have the potential to build Inuit adaptive capacity are the Fisheries Joint Management Committee (established under theInuvialuit Final Agreement), and the Nunavut Wildlife Management Board.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".