Bibliographic record
Abstract
Interview with Elem Klimov at the Denver International Film Festival 1986 Ron Holloway: Elem, how many films have you made up to now?Elem Klimov: Over the past 20 years, I have made six feature-length films and one short film lasting only 18 minutes, a film called Larisa (1980). It's a biography of my wife, Larisa Shepitko (1938-1979). It was made as a tribute to the filmmaker Larisa Shepitko. A small film - but one of the most difficult films of my life. Which is your favourite among the feature films?None. Which then was the easiest film to make? Or the most difficult?None of them were very easy to make. Maybe Sport, Sport, Sport (1970) was the easiest, but that was because I didn't take it very seriously. And the most difficult one among the serious films was definitely Idi I smotri (Come and See, 1985)....
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.063 | 0.019 |
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".