Inter-community Relations Factor on the Empowerment of the Aisle Community in Makassar City: A Structural Equation Model
Bibliographic record
Abstract
Background: There are several important factors in building community empowerment, one of them is the inter-community relationship. However, further research is rarely done on this dimension. The dimension of the relationship includes community structure, community strength, community motivation, community communication, community integrity, community participation, and community development. Objective: This study aimed to find the best model of the effect of the inter-community relationship on the condition of healthy aisle in Makassar City. Method: This research was conducted in 2 sub-districts in Makassar City, those are Rappocini District and Ujung Tanah District. The research samples involved were 560 households in Lorong. Data analysis was performed using multivariate analysis through the structural equation model (SEM) test. Results: The results of this study found that clear vision (CR = 3.219 and p = 0.01), capacity development (CR = 3.166 and p = 0.02), norms existence (CR = 3.143 and p = 0.02) on inter-community relationship significantly affected the aisle community empowerment. Conclusion: Inter-community relationship is the best model in developing a healthy aisle in Makassar City through solidarity, shared motivation, shared trust, clear vision, resources shares, capacity building, norms in society, government support, and community development. This study recommends that in order to maximize a clear vision for the aisle community working group, the government should carry out legality regarding the aisle community structure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".