Bibliographic record
Abstract
List of illustrations Acknowledgments Note on romanization List of contributors 1. Introduction: cinema and urban culture in republican Shanghai Yingjin Zhang Part I. Screening Romance: Teahouse, Cinema, Spectator: 2. Teahouse, shadowplay, bricolage: laborer's love and the question of early Chinese cinema Zhen Zhang 3. The Romance of the Western Chamber and the classical subject film in 1920s Shanghai Kristine Harris 4. The urban Milieu of Shanghai cinema, 1930-40: some explorations of film audience, film culture, and narrative conventions Leo Ou-fan Lee Part II. Imaging Sexuality: Cabaret Girl, Movie Star, Prostitute: 5. Selling souls in sin city: Shanghai singing and dancing hostesses in print, film, and politics, 1920-49 Andrew D. Field 6. The good, the bad and the beautiful: movie actress and public discourse in Shanghai, 1920s-1930s Michael G. Chang 7. Prostitution and urban imagination: negotiating the public and the private in Chinese films of the 1930s Yingjin Zhang Part III. Constructing identity: Nationalism, Metropolitanism: 8. Constructing a new national culture: film censorship and the issues of Cantonese dialect, superstition, and sex in the Nanjing decade Zhiwei Xiao 9. Metropolitan sounds: music in Chinese films of the 1930s Sue Tuohy 10. 'Her traces are found everywhere': Shanghai, Li Xianglan, and the 'Greater East Asia film sphere' Shelley Stephenson Filmography Notes Selected bibliography Character list Index.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".