The Leadership Role in The Smart-Village Program in Banyuwangi District, East Java
Bibliographic record
Abstract
The use of information communication technology (ICT) in government administration has changed the face of Weber's conventional bureaucracy into a modern model. The electronic or digital-based government model is an important milestone in this 21st century due to its ability to improve government performance, thereby increasing public satisfaction. The successful implementation of e-government is influenced by many beneficial and nonbeneficial determining factors, such as leadership. Therefore, this research aims to explore the leadership role of the Smart village program implemented in 2010 by the Banyuwangi District Government using a qualitative approach. Data were collected through observation in selected locations, interviews, and secondary sources. The data obtained were processed using the NVivo application process due to its ability to capture and build inductively concepts that describe research based on local perspectives. The results processed through the word cloud feature illustrate two interesting analyses. First Smart village is considered an online administration service by the Banyuwangi people because it is easy, fast, open, and full of certainty. Second leadership has 5 important roles, namely Charismatic, Innovative, Consistent, Cooperation, and Integrity. In conclusion, the success of e-government is strongly influenced by the usefulness of services for the community and the seriousness of the leadership to implement e-government not only as a political gimmick but to serve the community. Keywords: Digital service, Transformational Leadership, Digital Society, Openness
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".