Audible Efforts: Gender and Battle Cries in Classic Arcade Fighting Games
Bibliographic record
Abstract
Video games are demanding work indeed. So demanding that our screen heroes and heroines are constantly making sounds of strife, struggle, or victory while conducting surrogate labor for us running, fighting, saving worlds. These sounds also represent the very real demanding labor of voice actors, whose burnout and vocal strain have recently come to the fore in terms of the games industries’ labor standards (Cazden, 2017). But do heroes and she-roes sound the same? What are the demands—virtual, physical, and emotional—of maintaining sexist sonic tropes in popular media; demands that are required of the industry, the game program, and the player alike? Based on participatory observations of gameplay (i.e., the research team engaging with the material by playing the games we study), close reading of gendered sonic presence, and a historical content analysis of three iconic arcade fighting games, this article reports on a notable trend: As games self-purportedly and in the eyes of the wider community improve the visual representation of female playable leads important aspects of the vocal representation of women has not only lagged behind but become more exaggeratedly gendered with higher-fidelity bigger-budget game productions. In essence, femininity continues to be a disempowering design pattern in ways far more nuanced than sexualization alone. This media ecology implicates not only the history of best practices for the games industry itself, but also the culture of professional voice acting, and the role of games as trendsetters for industry conventions of media representation. Listening to battle cries is discussed here as a politics of embodiment and a form of emotionally demanding game labor that simultaneously affects the flow and immersion of playing, and carries over toxic attitudes about femininity outside the game context.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".