Introduction: #MeToo and the Visual Politics of Transnational Chinese Cinema
Bibliographic record
Abstract
: The #MeToo movement in Asia provides a window onto the visual politics of gender and sexuality in the transnational Chinese film industry. Women filmmakers do more than chronicle and critique the ways in which men look at women on screen. They also create ways of seeing in direct opposition to the male gaze. Taking up their cameras as activists, women filmmakers offer a gendered perspective on the political movements and events of the second decade of the twenty-first century. If #MeToo opens the door wider, these films by women may have a greater chance of being produced, distributed, programmed, reviewed, and even actually seen by viewers eager for relief from the constricting blindness of the male gaze. Keywords: Sexual harassment; state surveillance; oppositional gaze; intersectional gaze; male gaze; feminist activism In 2017, allegations against Harvey Weinstein opened up the depth and breadth of sexual harassment in Hollywood; Weinstein’s associates in Hong Kong and the People’s Republic of China (PRC) came under scrutiny as well. One of the first responses from mainland China came in the form of an opinion piece published in the English-language edition of the government-run newspaper China Daily . Sava Hassan, an expat Egyptian-Canadian residing in the PRC, wrote “Weinstein Case Demonstrates Cultural Differences” (2017), in which he argued that sexual harassment does not pose a problem in China because of the timidity of Chinese men and the prevailing conservative values of the society. Although claiming the article only reflected his observations as a teacher in China, Hassan’s ignorance of the actual situation did not belie the fact that China Daily published a perspective that thoroughly disregarded the facts (see Feng 2017). Internet response was immediate and China Daily expeditiously withdrew the piece; however, the Chinese press inadvertently added fuel to the growing transnational conflagration around the smoldering issue of sexual harassment and violence against women in China. In fact, Weinstein’s sexual misconduct points to a pervasive transnational culture of gender inequality that extends beyond Hollywood and connects industrial networks ranging from Europe through Northeast as well as South and Southeast Asia to other parts of the world (Roxborough 2017).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".