Perspectives of racialized immigrant communities on adaptability to climate disasters following the <scp>UN</scp> Roadmap for Sustainable Development Goals (<scp>SDGs</scp>) 2030
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Climate Change‐induced risk events intensify vulnerability and disproportionately affect regions and racialized immigrant communities. Understanding the multiple dimensions of disaster and risk, especially how these are embedded in a broader social‐political context, and translated into risk management strategies, have now been identified as priority areas under Sendai Framework for Disaster Risk Reduction 2015–2030 and UN Research Roadmap for achieving Sustainable Development Goals (SDGs) 2030. Drawing on a relational intersectional approach, this study explores the meanings of climate change disasters and risk reduction strategies from a racialized immigrant community's (i.e., Bangladeshi‐Canadian) lived experiences in Calgary, Canada. From our relational research, we learned that extreme climate events (such as forest fires/wildfires, heat waves, flash floods, severe colds, hailstorms, etc.) are the most common stressors unevenly impacting the household economy, physical health, and mental and psychological wellbeing of the racialized immigrant community in Calgary. The community's compounded vulnerability to disaster risks is further aggravated due to their intersectional positionality and structural inequality (systematic marginalization) rooted in the lack of explicit anti‐racist policy guidelines in Canada. The community members adapt diverse strategies (mostly reactive) based on their family income, severity and frequency of the exposure to risks, social support system, geographic location (residence), cultural practices, and involvement with community networks. While proposing solutions, they suggested that community‐engaged tailored disaster intervention strategy could play an instrumental role in addressing social vulnerability (determinants) and enhancing adaptive capacity at the local level. Moreover, this study calls for a more holistic account of the differential vulnerability context to better understand the structural root causes and emphasizes that upscaling land‐based practices and knowledge transmission, ensuring deliberate participation of visible minorities, fostering collective action and integrating local community associations into all stages of disaster management should be the priority for the state agencies to support long‐term resilience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it