TINGKAT KOHESI PERUMAHAN BERPAGAR TAMANDIPONEGORO, LIPPO KARAWACI, TANGERANG
Bibliographic record
Abstract
Strong cohesion within a community is reflected in its residents, where they feel a strong sense of togetherness, frequently interact, and engage in activities with neighbors, showing a keen interest in staying in the area. Conversely, communities with low cohesion experience the opposite. Gated communities are often promoted as ideal environments for fostering community ties due to the social-economic homogeneity of residents and enhanced security. Additionally, there is a connection between the level of community cohesion and the willingness to report or intervene in criminal activities. This study aims to assess the level of cohesion in the gated community of Taman Diponegoro using the Housing Cohesion Instrument developed by Buckner (1988), known for its reliability. The research covers Taman Diponegoro and Rolling Hills, which feature similar housing designs and prices. The survey method, along with second-order confirmatory factor analysis (CFA) and scoring, was used. Out of 154 members in the WhatsApp group, 48 participated. Results show that cohesion is moderate, with residents rarely visiting each other. The large number of housing units, over 100, aligns with the rise of individualism. To improve cohesion, limiting the number of homes in gated communities is recommended.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".