“You relied on God and your neighbour to get through it”: social capital and climate change adaptation in the rural Canadian Prairies
Bibliographic record
Abstract
Abstract Social capital is increasingly recognized as a key determinant of adaptive capacity to climate change. Beyond formal adaptation infrastructure like insurance and public disaster support programmes, it can be difficult to identify the role that informal social capital—such as relationships, trust, and mutual support between community members—plays in climate adaptation. Drawing on a multi-site qualitative study in the Canadian Prairie region, this paper examines how three forms of social capital (bonding, bridging, and linking) shape rural communities’ adaptation to climate extremes. Based on in-depth interviews with 163 community members, the findings demonstrate how social capital contributes to adaptive capacity, particularly in rural areas where more formal supports may be absent or lacking. We examine how social capital is affected by existing socio-economic sensitivities, such as rural depopulation, which can reduce informal social capital while simultaneously increasing people’s dependence on it. The findings indicate the strengths and limitations of bonding and bridging social capital, particularly in the face of future climate extremes that may exceed local adaptive capacity. Further, we find that informal social capital may also reinforce gender inequality, exclusion, and inter-group differences, indicating its limitations for socially inclusive adaptation. Addressing these structural factors can help communities move past coping and toward long-term adaptation. In the face of increasing climate risks, our findings suggest the importance of public supports that are attentive to local strengths, gaps, and social relations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".