Factors Affecting the Use of Shared Space and Environmental Facilities of Cibeureum Rental Social Housing, Indonesia
Bibliographic record
Abstract
Shared space and environmental facilities the primary supporting space for low-income residents identical to high social activities. However, in reality, some areas tend not used. This phenomenon indicates exist of other factors that influence its use. This study aims to define the factors that affect the use of shared space and environmental facilities at Cibeureum Rental Social Housing. This research uses the qualitative method by evaluating the use of space and explanation of its phenomenon with the qualitative explanation based on field theory and fact. Factors that affect the use of shared space and environmental facilities of the Cibeureum Rental Social Housing according to research conducted is suitability of the pattern of use of space residents as apply tradition settled and ability of its physical elements in accommodating activities. Users are still doing the action to meet the primary needs of people (clothing, food, and housing) and social events with special needs although space is far from occupancy, because simple social activities at a very close distance to the dwelling. Users are often doing optional activities such as relaxing and sitting are often performed in the nearest space to the dwelling. This research can develop the design of shared space and environmental facilities to consider the distance and type of activities based on suitability of occupant-based traditions of living.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".