Building digital governance ecosystem readiness for Indonesian regional representative council institution
Bibliographic record
Abstract
The Regional Representative Council of the Republic of Indonesia (DPD RI) faces the challenge of digital transformation. This research evaluates its digital readiness, comparing it with Estonia, South Korea, Singapore, and India, to bridge the digital divide between the central office and regional offices. Using gap analysis, Multi-Dimensional Scaling (MDS), and the Analytical Hierarchy Process (AHP), we found that infrastructure, institutions, and human resources are key factors. The AHP prioritizes human resources (43.9%), institutions (31.1%), and infrastructure (19.6%). The top programs include i-Parliament Literacy (40.3%), i-Parliament Management (39.6%), and Digital Infrastructure (20.1%), which have the potential to increase the readiness index to 76.43 (“VERY READY”). Currently, the DPD RI index stands at 56.05 (“READY”), indicating significant regional disparities, particularly in Eastern Indonesia, and lagging behind benchmark countries in terms of interconnectivity, security, and parliamentary digitalization. This prioritizes “soft infrastructure” (80%) over “hard infrastructure” (20.1%), aligning with initiatives such as the Digital New Deal 2.0 and Digital India. This study validates and develops the combined application of gap analysis, MDS, and AHP.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".