Digitalization of government and enhancement of community participation in development in North Sumatra province
Bibliographic record
Abstract
This study aims to analyze the relationship between government digitalization and the enhancement of community participation in development in North Sumatra Province. Digitalization is understood as an effort to transform bureaucracy through information technology, manifested in official regional government websites such as sumutprov.go.id, which provide various public services online. However, the effectiveness of such digital platforms in encouraging active citizen participation has not been widely explored, particularly in non-metropolitan areas. This research employs a descriptive qualitative approach with data collection techniques including observation, documentation, and semi-structured interviews with ten informants from various regions in North Sumatra. Data were analyzed using the Miles, Huberman, and Saldana model, supported by NVivo 15 software for thematic coding and data visualization. The findings reveal that although the public has access to digital government services, the level of engagement remains low due to structural barriers (internet access and digital literacy), cultural factors (offline habits), and institutional constraints (limited bureaucratic responsiveness). Based on the Diffusion of Innovation theory, most of the population falls into the late majority and laggards’ categories, indicating that digital innovation adoption is not yet widespread. This study offers novelty by specifically mapping the forms of digital community participation based on direct experiences and identifying the dynamics of digital innovation adoption within the context of regional governance. These findings have significant implications for the development of a more adaptive, inclusive, and participation-oriented digital government strategy in regional 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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".