Chinese Netizens’ Reactions to Red Classics Cinema Animation A Case Study of Taking Tiger Mountain by Strategy (2011)
Bibliographic record
Abstract
<p class="1Body">‘Red Classics’ can be considered as an important subgenre of Chinese cinema animation, which is often adapted from war novels, revolutionary model opera, songs and live-action films<em>. </em>Animated film <em>Taking Tiger Mountain by Strategy (2011)</em>, as a Red Classics cinema animation production, has provoked heated discussion among Chinese netizens in two largest online film communities and review aggregator sites <em>Douban Movie </em>and <em>Mtime</em>. This paper reveals the Chinese netizens’ reception process of this movie before and after public screening by analysing the relevant brief comments and longer film reviews from the above two websites. Chinese audiences’ expectation on <em>Taking Tiger Mountain by Strategy</em> was relatively negative before exhibition in cinema. This can be partly attributed to the emotional discontent over the previous Chinese animation and government intervention on artistic creation. However, there has been a gradual shift in the attitudes of netizens towards this movie after the public exhibition, the reviews have become more objective, rational and positive. This article aims to identify the reasons behind such attitude change by applying reception studies approach, and how and to what extent these elements have affected Chinese netizens’ evaluation process.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".