Analyse Des Ellipses dans Le Dialogue Des Vidéos Informations de TV5 Monde
Bibliographic record
Abstract
ABSTRAK Nanda Nurul Huda. NIM. 2123131035. “Analisis Ellipsis dalam dialog video informasi TV5 Monde”. Skripsi. Program Studi Pendidikan Bahsa Prancis. Jurusan Bahasa Asing. Fakultas Bahasa dan Seni. Universitas Negeri Medan. 2017. Penelitian ini bertujuan untuk mengetahui jenis-jenis ellipsis dan struktur kalimat yang menggunakan ellipsis dalam video dari l’émission 7 jours sur la planète di siaran TV5 Monde. Teori yang digunakan adalah teori Hasan dan Halliday (1975) ( dalam Togatorop 2014:17). Penelitian dilakukan di perpustakaan Fakultas Bahasa dan Seni, UNIMED. Metode digunakan yaitu deskriptif kualitatif. Sumber data yang digunakan adalah video yang terdapat dalam acara l’émission 7 jours sur la planète dari siaran TV5 Monde. Teknik yang digunakan adalah teknik menyimak dan mencatat. Hasil dari penelitian ini menunjukkan bahwa ada 33 ellipsis yang di temukan dalam 8 video, yang terdiri dari 16 jenis kata benda, 9 jenis kata kerja, dan 8 jenis kalimat. Di dalam novel menggunakan ellipsis kata benda karena kata benda bisa digambarkan secara konkret maupun abstrak. Penelitian ini juga menunjukkan 4 struktur kalimat yang menggunakan dalam video. Yaitu : GN, GV, GN + GV, dan GN + GV + GN. Kata kunci : tipe ellipsis, struktur kalimat, video, l’émission 7 jours sur la planète, TV5 Monde.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".