Shadow of a doubt: a network analysis of mentoring as a screen industry equity intervention
Bibliographic record
Abstract
Purpose Policies intended to encourage gender equity in the film industry are ramifying and take many forms. This paper uses social network analysis to assess the effectiveness of one popular equity policy, shadowing, a form of mentoring. In shadowing programs, women and gender minorities (WGM) are connected to more experienced members of the industry through attachment to their productions. Design/methodology/approach We constructed real collaboration networks based on film releases from 2005 to 2020 in three countries and simulated the effects that hypothetical shadowing interventions would have on the distribution of social capital in these networks. We implement different versions of the intervention, including different eligibility criteria for shadows and shadowees as well as isolating the additive effects on participants’ project portfolios. Findings We find that shadowing is effective in enabling WGM to access the strongest network positions, which are currently disproportionately occupied by men. However, we show that the primary reason that shadowing is effective in doing this is because it provides a second project affiliation to WGM in an industry where it is difficult to get past one’s first project. Originality/value Our study contributes to the literature on how mentoring policies affect people’s professional networks as well as scholarship on mentoring as a gender equity policy. We contribute novel evidence to debates about the efficacy of shadowing programs for WGM in the film industry. We suggest that shadowing can be effective as a tool for not only helping individual WGM advance their careers but also for structurally reconfiguring the distribution of power in project-based collaboration networks.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".