Crime, history and the making of Operation Hyacinth: An interview with Marcin Ciastoń
Bibliographic record
Abstract
Piotr Domalewski’s Operation Hyacinth (2021) centres on the young police officer Robert’s (Tomasz Ziętek) investigations into a case of serial murder and his developing romance with the student Arek (Hubert Miłkowski). The connective tissue is that all of the victims are gay men and Arek plays a role in it. As the film unfolds, we learn, alongside Robert, that the case is a massive cover-up operation and that his discoveries marry together his private and his professional lives. In this interview, I discuss, with writer Marcin Ciastoń, his extensive research for the film, which was inspired by historical events; and Robert’s and Arek’s romance. We explore how, if on the one hand, Arek represents rebellion to Robert, then, on the other, Robert is the embodiment of reticence, and we attend to a key scene wherein Robert is pressured by his father (Marek Kalita), also a police officer, into questioning Arek about his sexual history. All of the characters appear to be on trial, and as Ciastoń declares, ‘[e]veryone involved had a secret’. This interview advances scholarship by recovering the critical project that directly informs this imaginative one, by suggesting its importance in LGBTQ history and by attending to Ciastoń’s approach to personal and public histories.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".