PENGARUH PERUBAHAN FISIK LINGKUNGAN TERHADAP PERUBAHAN KESEJAHTERAAN MASYARAKAT PADA PROGRAM RELOKASI PERMUKIMAN BANTARAN SUNGAI BENGAWAN SOLO KOTA SURAKARTA
Bibliographic record
Abstract
Resettlement in Surakarta City caused by floods in 2007, but rises some problems after resettlement among other high price of housing land, the community economic which is low causes the low of house affordability too, and the government regulation which is less emphatic in limiting settlements growth in inappropriate land. It makes a problem how the implications of the physical environment to the changes of community welfare in resettlement programme of Bengawan Solo river bank in Surakarta City. The analytic-method of this research are paired samples t-test to know the changes of physical environment and community welfare, then regression linear to know the implications of the physical environment to the changes of community welfare. The result from this research is there is strong implication of improvement of physical environment to the changes of community welfare. The implication of two variabel is linear, it means that every improvement of physical environment have an impact to improvement of community welfare and otherwise. Keywords : squatter settlement, resettlement, physical environment, community welfare, paired samples t-test, regression linear
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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".