Making the grade: Feminine lack, inclusion, and coping strategies in digital games higher education
Bibliographic record
Abstract
The barriers faced by women in games production have been firmly established, including well-documented harassment and material forms of structural discrimination such as gender pay gaps. At the same time, the explanation that homogeneity in the games industry is due to a ‘leaky pipeline’ between training and the workforce persists, extending discourse familiar from the history of computing. Games higher education, the presumed feeder for diverse talent, remains underexplored despite the increasingly compulsory nature of university degrees in job postings. This article addresses the gap by exploring the experiences and perspectives of students studying games subjects in five UK universities. Based on thematic analysis of interviews, I argue that efforts to ‘get in’ to exclusionary tech spaces based on discourses of feminine lack fail to account for how these environments require marginalized people to develop strategies for coping with exclusionary norms to ‘stay in’.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".