PREFERENCES OF RENT GARAGE OWNERS IN LOCATION SELECTION IN DALUNG VILLAGE, BADUNG REGENCY
Bibliographic record
Abstract
<p>Dalung Village, a residential development area in North Kuta District, Badung Regency, has many densely populated residential areas, yet the areas are not supported by adequate parking facilities. It is then used by the community around the residential areas to develop a rental garage business resulting in the direction of land use in Dalung Village in the current settlement designation area developing towards trade and services. This study aimed to determining the factors that become rental garage owner’s preferences in choosing the location of a rental garage related to the characteristics of land use in Dalung Village. The research method used was a qualitative method with a case study approach. Data was collected through field observations, interviews and distribution of google form questionnaires to rental garage owner’s. The results of this study indicate that the community's preference were the factor of the residential environment with lots of parking on the shoulders, the density factor of the number of vehicles that passed on public roads and environmental roads, environmental safety factors, capital and maintenance efficiency factors as well as the presence of similar businesses (rental garage) in an residential areas. The factor that made land use occurred was the lack of firmness of the Dalung village government and Badung district government in make arrangement for regional spatial planning areas and providing sanctions for violations of village spatial planning.<strong></strong></p><p> </p>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".