TINGKAT KOHESI PERUMAHAN BERPAGAR TAMANDIPONEGORO, LIPPO KARAWACI, TANGERANG
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
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.
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.
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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it