Bibliographic record
Abstract
In this interview, writer – director Peeter Rebane and I discuss his feature Firebird (2021). Set during the Cold War, the film centres on the real-life Sergey Fetisov’s (the film’s co-writer Tom Prior), Roman’s (Oleg Zagorodnii) and Luisa’s (Diana Pozharskaya) love triangle, exploring the decisions they make and their attendant consequences. Rebane and I examine the challenges of filming some extraordinary material – from underwater shots of the young Sergey (Romek Uibopuu) and Dima (Gregory Kibus) to shots of dozens and dozens of people seated in a concert hall, and from flying sequences to theatre ones – on an independent film budget, and how Rebane has retained integrity to the project without making compromises. We attend to the story on which Firebird was based; Rebane’s and Prior’s fidelity to their source material; how they expanded it to show, more prominently, the social and political context in which it is set; and how they altered Luisa’s character to show her perspective. We discuss Rebane’s extensive research into and recreation of the film’s world, which includes meeting with the real-life Sergey, to whom it is dedicated; casting it; and studying documentaries and photos to create costumes for its many actors and extras. Finally, Rebane and I explore the process of distributing Firebird during the COVID-19 pandemic, which has included screening the film at numerous events worldwide. This interview provides insight into both the making and the distribution of this ambitious film adaptation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 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".