Assessing Climate Change Vulnerabilities of Ontario's Rural Populations
Bibliographic record
Abstract
Climate change causes considerable challenges for both urban and rural communities. Our study aimed at enhancing the understanding of climate change effects on rural populations. The study was promoted in Middlesex County library locations and on Middlesex County’s social media accounts; all residents of Middlesex County were eligible to participate. Through this method of convenience sampling, we successfully recruited 40 rural residents and conducted five focus group sessions. The study was conducted in Middlesex County, in southern Ontario, Canada, which provided a good representation of southern Ontario's rural communities. Thematic analysis was used to analyze the data collected in focus group discussions. Focus group discussions yielded four main themes and provided valuable insights on several climate change-related topics. The four identified themes are: frequent extreme weather events, access to food and safe drinking water, protection from vector-borne diseases, and living in a rural community. Our results indicate key parameters to address the climate change issues for rural residents and lead to a series of recommendations to revamp climate change policy at local, provincial, and federal levels. Study Participants commented on the need for adaptation skills concerning the physical and mental health aspects of increased indoor activity (avoiding natural spaces/pollution). This could also be an indicator/opportunity for future health programming and funding to support new realities. Future research is needed to develop effective local solutions with collaboration among government, business sectors, and rural residents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".