How the Rural Context Influences Social Capital: Experiences in Two Ontario Communities
Bibliographic record
Abstract
Social capital has shown potential for its ability to improve physical and mental health, although findings about social capital’s impact in rural areas have been less promising. The aim of this study was to shed light on how adults in two small towns of rural Ontario experience social capital in their daily lives, and to contribute to the broader literature about the relationship between social capital and rural health. This qualitative phase of a sequential mixed methods study used interpretive description to explore community interactions, social and recreational opportunities, and issues of inclusion and exclusion in two rural Southern Ontario communities. Forty adults of varying ages were recruited using convenience sampling and participated in one of eight focus groups or 13 individual interviews. Data was collected between August and December of 2017 and was analyzed concurrently. The rural context influenced the experience of social capital and residents’ opportunities for accessing it. The structural context was relevant to the social capital experience due to rural residents’ reliance on cars, limited opportunities for young adults, and high rates of rural poverty. The social context influenced social capital by way of rural familiarity and friendly social norms, lack of privacy, and long-established social networks. While there is no single experience of rural social capital, these findings offer a picture of how the rural context can shape individuals’ experiences and opportunities for social capital in ways that benefit some community members while marginalizing others. Implications for health and strategies for building rural social capital are discussed. Keywords: Social capital, context, interpretive description, rural
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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 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".