MODEL KERJASAMA ANTAR DAERAH DALAM PERENCANAAN SISTEM TRANSPORTASI WILAYAH METROPOLITAN BANDUNG RAYA (Inter-regional Cooperation Model of Transportation System Planning in the Greater Bandung Metropolitan)
Bibliographic record
Abstract
Decentralization policy in Indonesia gives development authority at local government level which cause fragmentation among regions. On the other hand, there are some government affairs that need to be managed jointly between several regions, so that inter-regional cooperation is necessary. Greater Bandung Metropolitan has rapid development from Bandung City to regions arround it. With the increasing of activity rate, good infrastructure planning including transportation system planning are needed. Decision making in planning implies many considerations, so that transaction cost such as information, negotiation, enforcement, and agency were involved. For the purpose of increasing public services, study of inter-regional collaboration models are needed. Based on the analysis, found that there are factors that will cause transaction costs such as unequal distribution of information, conflict of interest, actor who dominates, and lack of commitments of stakeholders. Then the results obtained that the model that appropriate to applied in Greater Bandung Metropolitan is jointly-formed authority, which is an institution consisting of representatives from each local governments and has the authority to execute policy in particular sector.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".