Konsep Pengembangan Kawasan REBANA: Memisahkan Fungsionalitas dan Branding Pengembangan Kawasan
Bibliographic record
Abstract
Abstract. The development of the REBANA area is closely related to the large-scale development of industrial areas with all their negative impacts on the environment. In fact, a concept called Polycentric Smart Region is ready to be implemented for regional development to support environmental desires while still making regional connectivity the biggest factor in regional attractiveness. The data collection method used in this research is literature study with content analysis as the analysis method. The results obtained are that the Polycentric Smart Region Development Concept can be a solution to the REBANA Area development issues because of the planned grouping of cities, relying on regional connectivity, and limiting development in non-urban areas. Abstrak. Pengembangan Kawasan REBANA sangat erat kaitannya dengan pembangunan kawasan industri secara besar-besaran dengan semua dampak negatifnya terhadap lingkungan. Padahal, sebuah konsep bernama Polycentric Smart Region siap diterapkan untuk pengembangan kawasan demi mendukung keberlanjutan lingkungan hidup dengan tetap menjadikan konektivitas wilayah sebagai faktor terbesar daya tarik kawasan. Metode pengumpulan data yang digunakan dalam penelitian ini adalah studi literatur dengan analisis isi (content analysis) sebagai metode analisis. Hasil yang diperoleh adalah bahwa Konsep Pengembangan Polycentric Smart Region dapat menjadi penyelesaian bagi isu-isu pengembangan Kawasan REBANA karena adanya pengelompokan kota yang terencana, bertumpu pada konektivitas wilayah, dan membatasi perkembangan di daerah non-perkotaan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".