Not All Fun and Games: Videogame Labour, Project-Based Workplaces and the New Citizenship at Work by Marie-Josée Legault and Johanna Weststar (Concordia University Press, 2024)
Bibliographic record
Abstract
NOT ALL FUN AND GAMES by Marie-Josée Legault and Johanna Weststar offers a timely and in-depth exploration of labour dynamics in the video game industry, shedding light on the labour relations and working conditions of those who create one of the most popular entertainment mediums in the world: video game developers. Rich empirical data collected over the span of fifteen years through surveying and interviewing video game developers is structured around four pillars of citizenship at work to present a comprehensive and complicated array of the economic, contractual, and social elements shaping the industry, and a nuanced portrayal of a diverse group of workers with varying levels of power, from industry stars to rank-and-file employees. Throughout the analysis presented in this book, the authors successfully demystify the existing industry narrative that obscures exploitative practises under the disguise of playfulness, creativity, and passion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".