Enhancing community resilience to climate change disasters: Learning experience within and from sub‐Saharan black immigrant communities in western Canada
Bibliographic record
Abstract
Abstract Enhancing community capacity towards resilience is key to reducing climate disaster risk, especially in Black immigrant communities in Canada. While there are many extreme climate change events occurring, such as hailstorms, floods, snowstorms, forest fires, droughts, and heat waves in western Canada, there is no known study that has explored resilience within sub‐Saharan African immigrant communities to climate disaster risks in western Canada. All these extreme climate change events have devastated Black populations threatening their ability to cope with disaster risks. Following a decolonial phenomenology methodological framework research approach; our study explores sub‐Saharan African immigrant communities' adaptation strategies to address climate disaster risk in western Canada. In this research, our main purpose was to investigate whether community resilience strategies implemented by the two provinces (Saskatchewan and Alberta) meet the unique needs of sub‐Saharan African Immigrants. By exploring local communities' perspectives on climate change, we highlighted the relevance of inclusivity in climate capacity building to reduce disaster risk and cope with climate change‐related disasters in the localities. Our findings revealed that personal experiences with climate change risks significantly influenced communities' strength and resilience and contributed to their resilience strategies. We view this paper as a first step in developing a community‐led climate change resilience research agenda that will have a practical application for the community in the face of climate change in Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".