The perils of project-based work: Attempting resistance to extreme work practices in video game development
Bibliographic record
Abstract
This article examines two blogs written by the spouses of game developers about extreme and exploitative working conditions in the video game industry and the associated reader comments. The wives of these video game developers and members of the game community decry these working conditions and challenge dominant ideologies about making games. This article contributes to the work intensification literature by challenging the belief that long hours are necessary and inevitable to make successful games, discussing the negative toll of extreme work on workers and their families, and by highlighting that the project-based structure of game development both creates extreme work conditions and inhibits resistance. It considers how extreme work practices are legitimized through neo-normative control mechanisms made possible through project-based work structures and the perceived imperative of a race or ‘crunch’ to meet project deadlines. The findings show that neo-normative control mechanisms create an insularity within project teams and can make it difficult for workers to resist their own extreme working conditions, and at times to even understand them as extreme.
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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".