Bibliographic record
Abstract
Women are underrepresented in the digital games industry all over the world. In Australia, womens level of contribution to game development is much lower than the USA, Canada, and UK. Reviewing literature from the areas of computer science, information technology, and digital games, this study focuses on the impact of social, structural and cultural aspects, and how these factors might influence women choosing a career in the Australian digital games industry. Using a mixed-method, Grounded Theory approach, a large-scale census of Australian digital game studios was conducted, and followed up by semi-structured interviews of a small group of women game developers. Findings reveal that the number of women game developers in Australia has recently increased, and although work culture stereotypes and poor workplace conditions persist overseas, Australian women are not experiencing these issues. However, getting interested in digital game development is still a major obstacle in convincing young women to enroll in game development degrees at university. Once enrolled though, attrition is a problem that has been attributed to teaching styles, lack of confidence and how male peers treat female students in their first year. Those women, who eventually graduate and pursue a career in digital games, more often share the influence of strong parents, male siblings, and enjoyed playing games from a young age.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.022 |
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".