Bibliographic record
Abstract
"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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.167 | 0.059 |
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".