ANALISIS PEMANFAATAN RUANG TERBUKA PUBLIK: STUDI DI TAMAN BERLABUH KOTA TARAKAN
Bibliographic record
Abstract
Urban parks are a form of public open space that can be used by anyone. In a city, the government needs to provide at least 30% of the city's area as open space. The Taman Berlabuh is one of the public open spaces provided by the Government of Tarakan City as an effort to respond it. Even though it has been built since 2016, the Taman Berlabuh was allegedly lacking management, thus some of the facilities damage and affecting its use. This study attempts to assess the level of effectiveness of using the Taman Berlabuh as a public open space in Tarakan City. The research used data collection techniques in the form of field surveys, interviews, and questionnaires. The sampling technique used was a simple random sampling of 100 park visitors. The analysis was conducted by using descriptive quantitative analysis through the scoring method. The research reveals that from the average of the three variables analyzed, i.e. the accessibility, facilities, and functions of Taman Berlabuh as a public space, the Taman Berlabuh is categorized as quite effective. The Taman Berlabuh provides multi-functions of open public space, they are as play, leisure and culinary facilities. The function of the Taman Berlabuh as a public open space has been optimal. While, when it observed from the variable availability of facilities and accessibility, it is still categorized as quite optimal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".