Kesenjangan Generasi dan Geografis dalam Literasi Digital: Implikasinya terhadap Kebijakan E-Government di Kabupaten Sorong
Bibliographic record
Abstract
Penelitian ini menganalisis paradoks literasi digital di Kabupaten Sorong, di mana skor indeks literasi yang relatif baik tidak sejalan dengan adopsi e-government yang rendah. Menggunakan pendekatan Mixed Methods dengan strategi Concurrent Embedded Design, penelitian ini menggabungkan survei terhadap 50 responden dan wawancara mendalam dengan pemangku kebijakan serta tokoh masyarakat. Hasil penelitian mengungkap kesenjangan generasi yang asimetris: Generasi muda memiliki kecakapan tinggi (Skor 78) namun mengalami Frustrated Agency akibat blokade infrastruktur, sedangkan generasi dewasa menghadapi Double Jeopardy (keterbatasan skill dan akses) dengan kerentanan keamanan yang tinggi (Skor 60). Temuan baru menunjukkan bahwa resistensi digital bukan sekadar masalah teknis, melainkan akibat Institutional Trust Deficit dan benturan budaya komunikasi lokal (High-Touch Culture) yang memandang teknologi sebagai entitas yang mengasingkan. Penelitian ini merekomendasikan strategi intervensi asimetris: penyediaan infrastruktur sebagai hak dasar, fasilitasi kanal ekspresi bagi pemuda, serta pendekatan layanan hibrida dan proteksi keamanan bagi generasi dewasa. Kata Kunci: Kesenjangan Digital, E-Government, High-Touch Culture, Kepercayaan Institusional, Kabupaten Sorong.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".