Kontribusi Ekonomi Digital Terhadap Pertumbuhan Ekonomi Lokal
Bibliographic record
Abstract
This study analyzes the contribution of the digital economy to local economic growth at the provincial level in Indonesia in 2024. The digital economy is measured using the Indonesia Digital Competitiveness Index score, while local economic growth is represented by Gross Regional Domestic Product per capita. This study also controls for two additional variables, which are capital expenditure and employee expenditure, to isolate the influence of the digital economy. The data used is secondary data from 38 provinces, analyzed using multiple linear regression. The results of the study show that the digital competitiveness score has a positive and significant influence on local economic growth. Provinces with higher digital scores tend to have higher GDP per capita. Employee expenditure also had a positive and significant effect, while capital expenditure showed no significant impact. In summary, the digital economy has proven to play a crucial role in driving local economic growth in Indonesia. Enhancing digital competitiveness and strengthening human resource capacity are relevant strategies for accelerating equitable development. This study recommends that local governments prioritize investments in digital infrastructure and digital skill development to support inclusive economic growth.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".