THE VIRTUAL CENSUS 2.0: A CONTINUED INVESTIGATION ON THEREPRESENTATIONS OF GENDER, RACE AND AGE IN VIDEOGAMES
Bibliographic record
Abstract
While many studies suggest media representations of marginalized social groups play a vital role in shaping one’s worldview (Gerbner et al. 1994) or normalizing power imbalances (Harwood and Anderson 2002), videogames continue to privilege characters that are White, adult and male. This paper revisits key questions addressed in Williams, et al.’s “The Virtual Census: Representation of Gender, Race and Age in Videogames” (2009) to examine how representations of gender, race, and age in videogames have changed over the last ten years. The present study analyses the United Kingdom’s top 100 best-selling games of 2017 and looks for changing and continuing trends in the representation of videogame characters compared to the original study. While our sample still shows a preference for White, adult, and male characters, a small but significant increase in the representation of female characters and people of colour offers hope for the future of gaming. By revisiting the 2009 census, we aim to provide empirical evidence that may contribute to further discussions of how gender, race and age are portrayed in videogames, both within academic and industry circles.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".