Bibliographic record
Abstract
At the end of Will Seefried’s new feature film Lilies Not for Me (2024), Owen (Fionn O’Shea), a writer, meets up with his former lover, Philip (Robert Aramayo), a doctor determined to cure him of his homosexuality. A subsequent scene shows Philip operating on the distressed Owen, who has protested he isn’t ill. Seefried’s film recovers two overlooked chapters in LGBTQ history, two different procedures meant to “cure” homosexuality: one, like Philip’s, that involves testicle transplants, and another, where gay men had “dates” with young nurses at institutions to learn about heterosexual courtship. A title card will reveal, near the end of the film, that “[a]n unreported number of homosexual men were subjected to testicular transplants in the 1910s and 1920s.” This work has been discredited. Seefried’s film is a critical hit. The film had its premiere in competition at the Edinburgh International Film Festival and it went on to earn the Prix du Jury at the Univerciné Film Festival. In this interview, Seefried and I discuss the making of this film.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".