Re-Distributing Gender in the Global Film Industry: Beyond #MeToo and #MeThree
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article draws on a big cultural dataset of over 130 million global screen times to consider the impact that the gender of a film's director has on the screening prevalence and geographic spread of new release feature films at the cinema. We compare results based on film screenings between December 2012 and May 2015 across a set of forty countries including the United States, France, Germany, Australia, Japan, India, and Brazil. This research is timely in light of renewed attention given to sexism and gender discrimination in the film industry. Rather than focus on the statistical paucity of women in production teams, where discrimination acutely diminishes workplace opportunities, our analysis instead focuses at the other end of the spectrum and identifies gendered patterns in film screenings across the globe. We correlate our findings to the social gender gap analysis undertaken by the World Economic Forum. We hope the research may be used as evidence to support nuanced policy innovations that can result in greater gender equality in the film industry and society more broadly.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it