Bibliographic record
Abstract
Since the early 1990s the media and cultural fields in China have become increasingly commercialized, resulting in a massive boom in the cultural and entertainment industries. This evolution has also brought about fundamental changes in media behaviour and communication, and the enormous growth of entertainment culture and the extensive penetration of new media into the everyday lives of Chinese people. Against the backdrop of the rapid development of China’s media industry and the huge growth in social media, this book explores the emotional content and public discourse of popular media in contemporary China. It examines the production and consumption of blockbuster films, television dramas, entertainment television shows, and their corresponding online audience responses, and describes the affective articulations generated by cultural and media texts, audiences and social contexts. Crucially, this book focuses on the agency of audiences in consuming these media products, and the affective communications taking place in this process in order to address how and why popular culture and entertainment programs exert so much power over mass audiences in China. Indeed, Shuyu Kong shows how Chinese people have sought to make sense of the dramatic historical changes of the past three decades through their engagement with popular media, and how this process has created a cultural public sphere where social communication and public discourse can be launched and debated in aesthetic and emotional terms. Based on case studies that range from television drama to blockbuster films, and reality television programmes to social media sites, this book will be of great interest to students and scholars of Chinese culture and society, media and communication studies, film studies and television studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".