Critical Success Factors of Public-Private-Community Partnership in Bali Tourism Infrastructure Development
Bibliographic record
Abstract
According to the National Development Planning Agency (Bappenas), the limited budget of the Government of Indonesia to improve public facilities can be resolved through the approach of Public-Private Partnership (PPP). PPP beneficial for the parties involved in such cooperation, among others, the transfer of technology, transfer of risk, and increase accountability. Until now, the PPP has not involve the active participation of the community, it is necessary to add an element of society in the so-called Public-Private-Community Partnership (PPCP). This study aims to investigate Critical Success Factors (CSF) of PPCP. CSF of PPCP obtained from the literature study of PPP. Respondents came from the regency/city level agency heads such as: the private sector at management level, party people represented by Indigenous Chairman (bendesa adat), penyarikan (secretary) and juru raksa (treasurer). Data of the questionnaire results collected resulted in a significant index (rate of interest) and subsequently analyzed with the “factor analysis“ to determine CSF of PPCP. This study resulted in CSF of PPCP by incorporating local communities into the PPP, which is an improvement proposal for Decree No. 13 of 2007 about cooperation between the government and the private sector in infrastructure. From the results of a factor analysis, obtained the nine CSF are: socio-cultural factors (values diversity of 29.914%), legal factors (14.198%), procurement factor (5.330%), risk factors (4.956%), a consortium factor (4.312%), technical factors (3.951%), economic factors (3.643%), financial factors (3.241%), and technological factors (3.224%).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".