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Record W2898451968 · doi:10.32396/usurj.v5i1.359

Promiscuous versus Romantic Lesbianism in Films

2018· article· en· W2898451968 on OpenAlexaffvenue
Bidushy Sadika

Bibliographic record

VenueUSURJ University of Saskatchewan Undergraduate Research Journal · 2018
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsQueerLesbianHollywoodMovie theaterHuman sexualityDepictionRomanceGender studiesMainstreamArtContext (archaeology)FantasySociologyLiteratureAestheticsHistoryArt historyPolitical scienceLaw

Abstract

fetched live from OpenAlex

There is a lack of queer visibility in the mainstream Hollywood films because of the Production Code Administration in October 1961 that prohibited the presentation of “sexual perversion” on screen. However, the amendment of this code and gay liberation movements in 1970s, and the era of “new queer cinema” in the 1990s increased media representations of sexual minorities. Based on this context, the article critically analyzes the depiction of lesbian characters and their romance in the films The Killing of Sister George (1968) and Blue is the Warmest Color (2013). The Killing of Sister George depicts the life story of June Buckridge who loses her career and her relationship due to her sexuality, and Blue is the Warmest Color is a story about Adele, who explores her sexuality as a teenager. Whereas the former film depicts lesbianism from a promiscuous lens, the latter approaches this topic from a realistic and contemporary perspective. Thus, the films represent lesbian sexuality in unique ways that allows us to investigate the progression of queer depiction in the mainstream cinema. The similarities and differences between the queer representation in the films, as well as the way the film portrayal of lesbian romance relate to the current research literature are discussed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.078
GPT teacher head0.376
Teacher spread0.298 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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