Unsur Keberpihakan Pada Pemberitaan Media Online Analisis Wacana Kritis Pemberitaan Kampanye pada Kumparan.Com
Bibliographic record
Abstract
Pemilu 2019 menampilkan memanasnya kampanye untuk merebut hati rakyat. Banyak media berlomba mengangkat berita teraktual seputar Pileg dan Pilpres. Dalam kondisi ini, media seharusnya berpihak kepada masyarakat dan bukannya menjadi alat kekuasaan atau kepentingan tertentu. Apakah media Kumparan.com benar-benar netral? Atau malah memihak kepada salah satu paslon. Peneliti melakukan analisis mendalam terkait teks media untuk mengetahui secara jelas dan gamblang makna, ideologi, keberpihakan dan kepentingan media. Tujuannya agar publik dapat menilai dan memilah informasi yang benar. Teori yang digunakan dalam penelitian ini adalah analisis wacana kritis dari Norman Fairclough, sedangkan metodenya adalah deksriptif kualitaif melalui teknik Analisis Wacana Kritis (Critical Discourse Analysis). Hasil penelitian menunjukkan bahwa pemberitaan Kumparan.com berpihak kepada salah satu Paslon Capres dan Cawapres Jokowi-Maruf.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.011 |
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".