Accelerating Urban Development in Indonesia: The Impact of Online Government Services
Bibliographic record
Abstract
In Indonesia, the transition to online bureaucratic services at the municipal level, encompassing areas such as population administration, education, procurement, public information dissemination, taxation, and civic engagement in development, represents a significant shift towards modernizing governance and enhancing socio-economic development.Despite the widespread adoption across various agencies, the integration of these services has not been uniformly achieved.This study delves into the factors influencing the preparedness of both the community and government apparatus in adopting an online model for government bureaucratic services.It is revealed that factors such as comprehension, proficiency in technology, psychological and ethical guidance, and both formal and informal education, along with tangible and intangible incentives, exert a positive and significant influence on the readiness levels of community members and government personnel to engage in online service provision.Moreover, it is demonstrated that online socio-economic program services serve as a critical mediator in expediting the development of urban areas.The findings underscore the necessity for municipal governments to enhance the comprehensive implementation of various online socio-economic services, as they are pivotal in accelerating urban development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".