Exploring holistic resilience in Nova Scotia: a study of Hall's Harbour
Bibliographic record
Abstract
Small rural communities in Nova Scotia grapple significantly with the impacts of coastal climate change and adaptation efforts target these environmental threats. However, socioeconomic factors also impact the resilience capacity of these communities. This research uses the Nova Scotian coastal community of Hall’s Harbour as a case study to explore holistic community resilience within the contexts of climate change and other socioeconomic considerations. Semi-structured interviews and a focus group with community members and other stakeholders serve as the data for the inquiry. Data analysis identified five themes that include: community values, coastal climate adaptation, tourism development, the communities’ capacity for resilience, and community economic and infrastructure development as the key factors supporting holistic resilience in the small rural coastal community of Hall’s Harbour. Key insights from the findings include: (i) building resilience through infrastructure development (ii) engaging the community capitals to enhance community resilience, and (iii) the significance of adaptive capacities in community adaptation and resilience planning. Based on the findings, the study suggests that effective community resilience building in the context of small coastal communities like Hall’s Harbour relies not only on climatic/environmental factors but on the combination of different socioeconomic influences within and outside the community. Key recommendations for resilience planning in small rural and coastal contexts include improved community engagement, diversified funding, stronger partnership, and capacity building.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".