Gaming Equity: Women, Videogame Companies, and Public Discourse
Bibliographic record
Abstract
It is by now well documented and widely acknowledged that the videogame industry has since its inception been a bastion of hegemonic masculinity. Only more recently, however, with events like Gamergate, #metoo, and the public accusations of workplace toxicity and sexism brought against prominent AAA giants like Riot and Activision Blizzard King, have game companies initiated policies and processes for change—or at least what looks like change, based on company websites and interviews with female employees. Does this mean women are being heard, at last? These are turbulent times for the industry, with legal actions, policy shifts, personal callings-out and billion-dollar corporate mergers and restructurings. What has changed and what is changing for women in games? How, and by whom, is that change being made? This paper begins with a closer look at what women have said since these events, about their experiences, expectations and frustrations working in the industry. Has the public scrutiny turned upon the games industry, post-gamergate and beyond influenced what women have to say about their conditions and experiences working in games? Are they better supported in taking the risks and shouldering the costs of speaking up? What workplace changes in policy or practice may have resulted from women giving public voice to their experiences? Building upon an earlier study of public speech by women about their experiences in the videogames industry (de Castell & Skardzius, 2019), this study both updates and extends its database, and deepens its analysis, by looking explicitly at a speech event’s context of elicitation: who elicits the “event” of public speech, on what topics, with what purpose? Through that dialogical lens we can make more visible and explicit how minority self-representation and marginalized identities and voices are deployed to bolster business as usual, even as they are still expected to lead the charges and fight the battles for a just and inclusive working life in games.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".