On the basis of risk: How screen executives’ risk perceptions and practices drive gender inequality in directing
Bibliographic record
Abstract
Abstract This paper explores how gendered perceptions of risk drive gender inequality. It does so by applying an Intersectional Risk Theory (IRT) framework to new empirical data on gender equality initiatives in the Canadian screen industries. The paper shows (1) that gendered risk perceptions constrain women directors’ work opportunities; (2) that the construction of gendered risk perceptions (“doing risk”) is shaped by the screen industry context and social inequalities generally; and (3) that practices of constructing risk perceptions can be disrupted and changed, which creates opportunities for a “re‐doing” or “un‐doing” of gendered perceptions of risk and offers new analytical perspectives onto the efficacy of gender equality initiatives. By interrogating how perceptions of risk inform decision‐making, the paper contributes new understandings of the drivers of systemic and intersectional inequality as a defining characteristic of work and labor markets in the screen industries and in the creative industries more broadly.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".