Not All Fun and Games : Videogame Labour, Project-based Workplaces, and the New Citizenship at Work
Bibliographic record
Abstract
Motivated by the goal of understanding the labour conditions of workers in the videogame industry and their participatory power to create decent work, Not All Fun and Games is a critical examination of a global entertainment juggernaut with revenues that top film, television, and music production combined. Jobs in the industry are heralded as the vanguard of the new economy, governments offer lucrative tax credits to lure game studios to their regions, and game developers often express commitment and passion for their work. Yet, the industry is also known for its toxic workplaces. To understand these disparities and gain insight into twenty-first-century labour conditions, Marie-Josée Legault and Johanna Weststar have carried out a comprehensive mixed-methods study of the North American industry over the past fifteen years. They combine detailed survey data from thousands of game developers with over one hundred qualitative interviews to systematically reveal labour issues such as precarity, lack of workforce diversity, unpredictable schedules, unpaid overtime, low unionization rates, worker burnout, and significant pay inequality. Updating the theoretical concept of citizenship at work, the authors connect these labour issues to a fundamental lack of voice and representation in the workplace. They determine that videogame workers and others in contemporary project-based work environments lack agency in regulating their work and lack fundamental protections. Not All Fun and Games comprehensively documents conditions in the North American industry and highlights ways to counter workers’ lack of voice and representation in their workplaces to better create healthy, equitable, and inclusive workplaces.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".