Bibliographic record
Abstract
Video games and their history are mostly seen from a masculine standpoint. Most traces, commentary, workers, communities, significant events or people, etc., are linked to a masculine lens that tends to ignore or marginalize women in video games and their culture. Even if they were clearly minorized in a masculine and sometimes hostile environment, there is a need to observe a part of history that gives us more information on the thought, the production, the influence, and the discourses of women without limiting them to the status of passive victims or to the margins of history. This article uses methods inspired by cultural history and textual analysis to investigate women’s discourses about women protagonists present in the game reviews of the specialized press covering video game culture and the video game industry. By doing so, we will observe a complex situation where different, and sometimes contradicting, intentions can be linked to how women characters are described, criticized, or mentioned in the reviews. As such, this analysis will show a cultural context where women’s writings are sometime influenced by the masculine hegemonic discourses made by or for a mostly gender restricted definition of the ‘gamers’, while other women’s text openly resist this hegemony by criticizing the way the many protagonists and women are represented. Women, their writing, and traces of their intention, can be seen in multiple magazines from 1981 to 2021. As such these public discourses are a small but important part of a more general and diverse history of video games and their communities.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.184 | 0.120 |
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".