Relation between neighborhood socio-economic characteristics and social cohesion, social control, and collective efficacy: Findings from the Boston Neighborhood Study
Bibliographic record
Abstract
Little is known about the determinants of collective efficacy, a neighborhood social process comprised of social cohesion and social control, which has shown to be beneficially associated with health. Our goal was to identify determinants of collective efficacy, social cohesion and social control. We used data collected from the Boston Neighborhood Survey, a cross-sectional survey conducted in 38 Boston neighborhoods in 2010 (n = 1710). We used multi-level logistic regression analyses to identify the relationship between the neighborhood-level characteristics and collective efficacy, social cohesion, and social control. High social fragmentation was associated with a decreased likelihood of reporting high collective efficacy (OR = 0.71, 95% CI = 0.54,0.95). and high social cohesion (OR = 0.63, 95% CI = 0.46, 0.86). High social fragmentation (OR = 0.80, 95% CI = 0.64, 0.99), and moderate economic deprivation (OR = 0.64, 95% CI = 0.47, 0.88) were associated with a decreased likelihood of reporting high social control, while high trust in police was associated with an increased likelihood in reporting high social control (OR = 1.86, 95% CI = 1.16, 3.00). Further research should be undertaken to better understand the direction of effect of these associations and how interventions to promote social processes can utilize these findings to improve health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".