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Record W4379793622 · doi:10.54254/2753-7048/5/20220411

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

2023· article· en· W4379793622 on OpenAlexaff
Xiaoke Wei, Yalu Yang, Zexin Zhang

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsQueen's University
Fundersnot available
KeywordsGazeMale gazeRomanceChinaGender studiesPsychologySociologyPolitical sciencePsychoanalysisLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.354
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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