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Record W7014157987

Only Love Matters

2023· other· en· W7014157987 on OpenAlexaboutno aff

Bibliographic record

VenueUEA Digital Repository (University of East Anglia) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsConversationDramaRepresentation (politics)RomanceShot (pellet)JournalismFeature filmHuman rightsPhotojournalism
DOInot available

Abstract

fetched live from OpenAlex

"Only Love Matters" is a groundbreaking romantic drama feature film directed by Kamran Qureshi, with a screenplay by Iannis Aliferis, and produced by Iram Qureshi for KQ Movies Ltd. The film, shot across the UK and the Great Indian Desert, tells the poignant story of an intersex mother, her sacrifices for her adopted daughter, and the broader struggle for the rights of intersex individuals. As the first British fiction feature to authentically portray intersex lead characters, Only Love Matters significantly contributes to the visibility and representation of intersex individuals in visual culture. The film has garnered considerable recognition, participating in multiple international film festivals and winning over 30 awards. Notably, it received the UEA's Engagement Award and was a finalist for the Innovation and Impact Award. The production involved more than 300 collaborators from around the world, including actors, crew, screenwriters, lyricists, music composers, singers, and facilitators, and was filmed across 40 diverse locations. Only Love Matters has played a pivotal role in raising global awareness of intersex issues. The film's team has engaged in numerous public discussions through various media platforms, including BBC One TV, BBC Radio, BBC News, Los Angeles Wire, The Telegraph, New York weekly, London Daily Post, That’s TV UK, Canada One TV, PTV News, podcasts, live TV broadcasts, and digital news outlets, thereby amplifying the conversation around intersex rights and representation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.038

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 head0.010
GPT teacher head0.180
Teacher spread0.171 · 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 designNot applicable
Domainnot available
GenreOther

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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