Female Gaze From the View of A Female Director Analysis of The Bold, The Corrupt, and The Beautiful and The Old Town Girls as Examples
Bibliographic record
Abstract
Since Laura Mulvey innovated the gaze theory, European and American societies have experienced two waves of feminist revolutions, and women's social status has gradually increased. Thus in recent years, many filmmakers have begun to create films from women's perspectives, and critics have also created the term "Female Gaze" as an opposite to the term "Male Gaze". However, there has not been any clear definition so far of what the "Female Gaze" is. The film and television analysis nowadays pays more attention to the female-to-female gaze and female-to-male gaze in romantic relationships and ignores the role of female directors play in shaping the relationship of film characters, especially the non-romantic relationships between female characters. Influenced by the feminist current, China's feminist films have also made great progress. Male and female directors have created new female roles through the lens. However, the different portrayals of female characters in the female-led films reflect the differences between Male Gaze and "Female Gaze". This paper will take The Bold, the Corrupt, and the Beautiful and The Old Town Girls as examples, by analyzing these two female-led films directed by a male and a female respectively, this paper will explore the main features of "Female Gaze" in female directing films.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".