Bibliographic record
Abstract
This paper explores how the film Rouge (1987) adapts and transforms traditional ghost narratives and how the cinematic anxiety of time is associated with the countdown temporality of Hong Kong in the 1980s. I argue that Rouge transforms two narrative structures of traditional Chinese literature — Caizi-jiaren (scholar-beauty) and the “historical ghost tale” — to foreground the particular temporality of Hong Kong. Firstly, the returning of the female ghost and her failure in pursuit of love intensifies the conflict between the modern linear time and the cosmological ghostly time and poignantly manifests the impossibility of a fifty-year unchanged commitment. Secondly, unlike traditional “historical ghost tales” in which ghosts were called back by traumas of the collapse of old dynasties, the revenant of the heroin in this film returns to the living world for the prearranged trauma of the future, due to the particular temporality of countdown Hong Kong has confronted since 1982. The countdown forced Hong Kong to enter a circular time and to experience the prearranged calamity in the future. Thus, I contend that this film rehearses a demise of Hong Kong, which exacerbates, rather than alleviates, the anxiety and pain associated with the traumatic experience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".