Gender (im)balance in the Russian cinema: on the screen and behind the camera
Bibliographic record
Abstract
The relationship between on-screen and off-screen inequality in film industries and the relative impact of these on movie attendance is widely discussed but not necessarily empirically demonstrated. This article examines the binary gender composition of film project teams and the gendered representation of film characters as factors for cinema attendance. We collected a unique dataset (N=1285) of all films released during the pre-pandemic decade (2008-2019) in Russia – at that time the largest European cinema market. A marked-up subset of 243 films was used to calculate a novel version of the Bechdel-Wallace test that accounts for the proportion of all non-stereotypical dialogues in the film narration, as opposed to the classical binary test. Our test proves very informative, revealing a strikingly high proportion of dialogues with stereotypical portrayals of women even among the films that pass the Bechdel-Wallace binary threshold. We also undertook a social network analysis (SNA) of the characters’ communications. This analysis demonstrate that women predominantly occupy a peripheral position in film plots. Both stereotyping and marginalization of women are positively related to the proportion of men in the film crew, especially in the role of screenwriter. Simultaneously, having more men in key positions is also correlated with access to larger budgets and better distribution, thus effectively impeding films with stronger women characters from wider audiences. These audiences, however, show no prejudice towards films with such characters: after 2015, films featuring central women protagonists have the same level of attendance as movies without them. Although Russia exemplifies a large non-Western cinema market, the trends we identify, particularly the “gatekeeping” effect of male filmmakers, is notably in line with those observed in Western democracies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".