Gender equity policy and visibility politics in the film and television industries
Bibliographic record
Abstract
This article improves our understanding of visibility in relation to gender equity policy in the film and TV industries and beyond, where visibility is typically imagined to be positive or beneficial. Drawing on new empirical data from an internationally comparative study of gender equity policy in film and TV in the UK, Canada and Germany, we identify three imaginaries of visibility in relation to gender equity policies: visibility is imagined as evidencing problems, as providing a solution and as demonstrating action. We show that imagining visibility in these ways limits the scope of gender inequities considered for policy intervention and creates the potential for counter-productive unintended consequences. We argue that advocating for, developing and implementing effective gender equity policy requires challenging and complicating current ideas of how visibility works in policy making. As the visibility of marginalised groups is so central to gender equity, yet rarely approached critically by policy makers, this article makes an important contribution to the literature around equity in public policy broadly.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".