The Research on the Representational Strategies of Femme Fatale in Contemporary Chinese Film-noir
Bibliographic record
Abstract
In 2014, Chinese director Diao Yinan’s feature Black Coal, Thin Ice wins the Golden Bear Award for Best Director at the 64th Berlin Film Festival, sparking enthusiastic discussion on the prospect of the so-called 'Chinese film-noir'. Among the classic noir elements that sparked discussion in these films, the lethal woman, or the ‘femme fatale’ stands out to be the one that attracts the most attention. In the span of a decade, the Chinese femme fatale varies quite enormously in her role in the narrative and the gender implication her relationship with the noir hero represents. This essay will build on Mark Conrad’s definition of film-noir, Jack Boozer’s categorization of femme fatale, and Elisabeth Bronfen’s tragic theory to analyze the construction of femme fatale in 2010s Chinese film noir. Equipped with these theoretical tools, this essay will focus on recent Chinese films-noirs and draw connections between their different representational strategy of the femme fatale to complete their noir narrative. This essay argues that while films like the Hunt Down (2019) and Long Day’s Journey Into Night (2018) approaches the lethal woman in the classic androcentric or even misogynist way, Black Coal, Thin Ice (2014) and the Wild Goose Lake (2019) endows her with more subjectivity and agency while complicating her relationship with the noir hero.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".