ASSESSING INVESTMENT REQUIREMENTS TO ATTAIN A 7% ECONOMIC GROWTH RATE IN BANDUNG REGENCY BY 2030
Bibliographic record
Abstract
Achieving the ambitious economic growth targets mandated in the Bandung Regency's Regional Medium-Term Development Plan (RPJMD) requires a solid investment planning foundation. This study aims to examine the investment needs required to drive the acceleration of regional economic growth until 2030. Using a quantitative approach through Incremental Capital-Output Ratio (ICOR) analysis based on historical GRDP and GFCF data, this research measures the level of investment efficiency and projects the required capital amount. The analysis results indicate that Bandung Regency has a reasonably good level of investment efficiency. However, to achieve the established economic growth targets, a significant increase in investment volume beyond historical realization is necessary. This finding implies that the regional development strategy must focus on two main pillars: not only attracting large volumes of investment but also maintaining and enhancing investment efficiency (keeping the ICOR low) through improvements in the business climate, human capital quality, and directing investment towards high-value-added sectors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".