DAMPAK LAYANAN INTERNET GRATIS UNTUK PELAJAR KELAS VIII SMPN 3 MAPAT TUNGGUL SELATAN PANGIAN KABUPATEN PASAMAN
Bibliographic record
Abstract
Layanan internet gratis ini diberikan kepada masyarakat Pangian khususnya untuk pelajar di SMP Negeri 3 Mapat Tunggul Selatan untuk mempermudahkan peserta didik dalam mengerjakan tugas sekolah. Tetapi kenyataannya pelajar kelas VIII di SMP Negeri 3 Mapat Tunggul Selatan tidak menggunakan layanan inernet gratis untuk kebutuhan Sekolah. Adapun tujuan penelitian ini adalah mendeskripsikan dampak layanan interenet gratis untuk kelas VIII Di SMP Negeri 3 Mapat Tunggul Selatan Jorong 3 Pangian Kabupaten Pasaman. Teori dalam penelitian ini yaitu teori belajar behavioristik. Pendekatan penelitian yang digunakan adalah kualitatif dengan tipe deskriptif. Informan penelitian purposive sampling. Teknik pengumpulan data berupa observasi, wawancara, dan studi dokumen. Unit analisis dalam penelitian ini yaitu kelompok. Analisis data menggunakan model Miles dan Huberman. Adapun dampak positif layanan internet gratis untuk pelajar Jorong 3 Pangian Mapat Tunggul Selatan Kabupaten Pasaman terdiri dari: (1) Memudahkan Dalam Proses Pembelajaran, (2) Sebagai sumber belajar, dan (3) Tidak mengeluarkan biaya. Sedangkan dampak negatif layanan internet gratis untuk kelas VIII di SMP Negeru 3 Mapat Tunggul Selatan Jorong 3 Pangian Kabupaten Pasaman terdiri dari: (1) Kecanduan game online, (2) Timbulnya sikap kurang menghargai, (3) Tidak disiplin mengantarkan tugas sekolah, (4) Tidak ada kontrol dari orang tua.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.013 |
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".