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Record W4367157159 · doi:10.1386/jepc_00047_7

Crime, history and the making of Operation Hyacinth: An interview with Marcin Ciastoń

2022· article· en· W4367157159 on OpenAlexaff
Tom Ue

Bibliographic record

VenueJournal of European Popular Culture · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOfficerRomanceScholarshipSociologyLesbianSexual abusePsychoanalysisLawCriminologyGender studiesPsychologyPolitical sciencePoison controlSuicide prevention

Abstract

fetched live from OpenAlex

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.287
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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