Community‐based adaptation research in the Canadian Arctic
Bibliographic record
Abstract
Community‐based adaptation ( CBA ) has emerged over the last decade as an approach to empowering communities to plan for and cope with the impacts of climate change. While such approaches have been widely advocated, few have critically examined the tensions and challenges that CBA brings. Responding to this gap, this article critically examines the use of CBA approaches with Inuit communities in Canada. We suggest that CBA holds significant promise to make adaptation research more democratic and responsive to local needs, providing a basis for developing locally appropriate adaptations based on local/indigenous and Western knowledge. Yet, we argue that CBA is not a panacea, and its common portrayal as such obscures its limitations, nuances, and challenges. Indeed, if uncritically adopted, CBA can potentially lead to maladaptation, may be inappropriate in some instances, can legitimize outside intervention and control, and may further marginalize communities. We identify responsibilities for researchers engaging in CBA work to manage these challenges, emphasizing the centrality of how knowledge is generated, the need for project flexibility and openness to change, and the importance of ensuring partnerships between researchers and communities are transparent. Researchers also need to be realistic about what CBA can achieve, and should not assume that research has a positive role to play in community adaptation just because it utilizes participatory approaches. WIREs Clim Change 2016, 7:175–191. doi: 10.1002/wcc.376 This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".