Female game developers in the Australian digital games industry
Bibliographic record
Abstract
Females make up half the population, and represent 47% of the digital game player market in Australia, yet women do not have comparative input and influence into the creation of digital games. Women are underrepresented in the digital games industry all over the world. In Australia, women’s level of contribution to game development is much lower than the USA, Canada, and UK. This thesis seeks to establish the factors that influence the low participation of women in this fast-growing industry, and to possibly find ways to reverse this trend. Design/methodology/approach – A review of literature from the areas of computer science, IT, and digital games development was carried out focusing on the impact of social, structural and cultural factors, and how these may influence women choosing a career in the Australian digital games industry. This study is empirical in nature using a Mixed-Method Grounded Theory approach. A database of all known Australian digital game companies was constructed. A census was then carried out with 356 digital game development studios across the country. From there, a separate “Aussie Women Game Developers” survey was conducted with thirty-five women working in the industry. From the survey respondents a subset of ten women participated in a semi-structured, in-depth, one-on-one, open-ended interview.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".