Individual and Community Factors Affecting Psychological Sense of Community, Attraction, and Neighboring in Rural Communities<sup>*</sup>
Bibliographic record
Abstract
L'échelle de Buckner, qui comporte trois dimensions—sentiment communautaire (SC), attractivité et voisinage—, fut appliquée à 1,995 personnes de 20 villages canadiens afin d'y mesurer le sentiment de cohésion sociale. Le nombre d'enfants, un revenu dépassant 20,000 $, l'âge, le lieu de naissance et le nombre d'années dans la collectivité exercent une influence positive sur le SC et l'attractivité. Le nombre d'enfants, un revenu dépassant 40,000 $, le lieu de naissance et le nombre d'années dans la collectivité influent de manière significative sur le voisinage. L'interaction accroît généralement la cohésion sociale individuelle. La localisation sur une île étant la seule variable communautaire significative, les politiques individuelles sont à privilégier pour accroître la cohésion. One thousand nine hundred ninety‐five individuals in 20 rural Canadian communities were measured on perceived social cohesion by the three Buckner scale subdimensions: psychological sense of community (PSOC), attraction, and neighboring. Number of household children, income over $20,000, age, birthplace in, and years lived in the community significantly positively influenced PSOC and Attraction. Number of household children (positive for income over $20,000; otherwise negative), income over $40,000, birthplace, and years in the community significantly influenced neighboring. Increased interaction generally increases individuals' social cohesion. As the only significant community variable was being on an island province, individual‐oriented policies are recommended to increase cohesion.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".