Engaging Communities in Adaptation to Climate Change by Understanding the Dimensions of Social Capital in Atlantic Canada
Bibliographic record
Abstract
This paper examines the role of social capital and its influence on the capacity of coastal communities in Atlantic Canada to respond and adapt to climate change, especially when dealing with extreme weather events. Three elements of social capital—social trust, institutional trust, and social networks—were considered. They were analyzed based on four questions targeting social capital during semi-structured interviews on climate change adaptation in 10 rural coastal communities located in three Canadian provinces (Quebec, New Brunswick, and Prince Edward Island). Results showed that these communities exhibited strong social capital, mainly because of a high level of social trust. People were ambivalent in the way they connected to institutions, especially with governments. They often felt isolated and left to themselves to deal with climate change adaptation decisions. The research conveys the difficulties and challenges of multilevel governance, where coastal communities generally ensure trust within the community first before trusting higher levels of government. Initiatives to improve public engagement and participation in decision making should be supported for further adaptation, although they would require greater accountability and transparency.
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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 | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".