Bibliographic record
Abstract
The Wardrobe of Cinema Costume is an essential element in cinema. It has been used widely as a storytelling device to distinguish characters, time, geographical location and cultural background of the story. This video essay examines the role of costume in films by using Qipao, particularly in the film In the Mood for Love (Wong Kar-wai, 2000). In the film In the Mood for Love , the art director William Cheung has put a great effort into using costume as a storytelling device by designing over 20 different types of qipao for the protagonist Su Lizhen. Qipao, a type of traditional Chinese fitting dress, peaked its popularity in Hongkong during the 1960s. Therefore, Qipao as a costume that has a representative time period is helpful for spectators to distinguish the era and location that the story took place. The video essay has catalogued the 20 different types of Qipao into three major categories depending on the colour, pattern, frequency of appearing and the interaction with the mise-en-scene. Then, it interprets the role of Qipao by breaking down the central relationship into three phases based on the categories. The differentiation of colours and patterns of the protagonist’s Qipao has reflected the complex inner world of the protagonist Su Lizhen in each stage. Su Lizhen’s delicate costume is an efficient cinema language to enrich the personality of the character and complement the storyline.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".