Analisis Wacana Kritis Masalah Sosial dalam Serial Drama Squid Game
Bibliographic record
Abstract
Salah satu serial terpopuler Netflix ialah Serial drama Squid game yang mengandung alegori besar masyarakat. Masalah sosial pada dasarnya merupakan suatu kondisi kehidupan dalam masyarakat yang tidak diinginkan atau suatu kondisi kehidupan yang menimbulkan persoalan. Masalah sosial dapat terjadi karena adanya hambatan dalam pemenuhan kebutuhan, akibat perubahan sosial ekonomi serta penggunaan ilmu pengetahuan dan teknologi. Penelitian ini bertujuan untuk membongkar wacana masalah sosial dengan metode analisis Wacana Kritis milik Norman Fairclough, berfokus pada “ketidakberesan” fenomena sosial pada serial drama Squid game. Penelitian ini merupakan penelitian kualitatif. Teknik pengumpulan data melalui observasi, dokumentasi dan studi pustaka. Teknik analisis data yang digunakan ialah teknik analisis Norman fairclough, dengan proses analisis yaitu mengamati subjek, objek, komposisi dan unsur tersirat yang merepresentasikan masalah sosial. Sedangkan teknik pemeriksaan keabsahan data menggunakan triangulasi sumber. Hasil penelitian merupakan kesimpulan analisis yang sudah dilakukan dimana peneliti memperoleh sebanyak 30 scene yang mengandung unsur masalah sosial. Hasil analisis menunjukkan bahwa serial drama squid game mengandung aspek yang membangun fenomena masalah sosial dari segi komposisi, dialog, dan praktik sosial di dunia nyata. Hasil analisis juga merupakan kritik secara tidak langsung terhadap permasalahan sosial masyarakat yang terjadi.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 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".