Bibliographic record
Abstract
The article demonstrates the examples of sexism in modern gaming. The authors regard thisphenomenon as natural. It originated in the 1980s, when men became the main consumer of computer games. Thus, cyberspace has become a “closed area” where male business owners have paid male programmers money to create computer games for men. In the 21 st century, the situation has changed dramatically: almost half of today's gamers are women, and there are many women among video game developers and business owners, but the approach to portraying characters in games has remained the same.The authors stress the importance of the activities of Canadian-American blogger Anita Sarkeesian, who was one of the first to raise the issue of sexism in video games in 2012. The activity of a feminist provoked a whole lot of discussion not only among the gamers. The attacks on Anita Sarkeesian's life and the numerous threats that came to her have attracted the attention of the United NationsThe most notorious sexist scandal in cyberspace was the so-called “gamergate” (2014). Programmer Zoë Quinn and her followers have fallen victim to harassment by colleagues and gaming men. As a result of media attention to gaming and active civic activism of Anita Sarkeesian, the international community has drawn attention to sexism in video games. Computer programmers, despite the additional material costs, began to add more female characters to games, more often to portray women as positive heroines with real body proportions and in appropriate clothing. The Committee of Ministers of the Council of Europe has adopted a Recommendation on Preventing and Combating Sexism (2019), presenting a video and an initiative webpage on eradicating sexism with the hashtag #StopSexism and the slogan “Sexism: See it. Name it. Stop it”.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".