THE VIRTUAL CENSUS 2.0: A CONTINUED INVESTIGATION ON THEREPRESENTATIONS OF GENDER, RACE AND AGE IN VIDEOGAMES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it