Social Diversity Acceptance and Community Attachment: Why and How Community Contexts Matter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Despite a growing body of literature on social diversity and community attachment, the exploration of how the relationship between social diversity acceptance and community attachment varies across country contexts remains a relatively unexplored area. This study seeks to address these knowledge gaps. Using data from the 2017–2022 Gallup World Poll, we examine the relationship between acceptance of social diversity and community attachment and explore how this relationship is moderated by residential satisfaction and confidence in the local economy. Multilevel regression models are employed to consider both individual and country‐level characteristics. The study revealed that social diversity acceptance is associated with community attachment, pronounced among individuals residing in low‐income countries. The relationship between social diversity acceptance and community attachment is influenced by two key factors: residential satisfaction, which includes access to social services and infrastructure, and confidence in the local economy. However, environmental residential satisfaction does not significantly mitigate the adverse effects of decreased acceptance of social diversity on community attachment. This study sheds light on the dynamic association between social diversity acceptance, community contexts, and community attachment. Our findings highlight the importance of community resources and advocate for inclusive policies to strengthen community attachment and enhance residents' well‐being.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.031 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.003 |
| 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 it