Building Momentum for Collectivity in the Digital Game Community
Bibliographic record
Abstract
Studies of digital game labor have tended to document problems in the working lives of developers while devoting relatively limited attention to solutions, or to collective representation as a step toward solutions. An increasing number of game developers are dissatisfied with their working conditions, and dissatisfaction is a necessary condition for workers to engage in collective action to gain the representational power needed to achieve change in the workplace. Noting that the landscape of collective mobilization in the game industry has not yet been systematically mapped, this article documents collective actions over the past five decades, and asks, “Are the collective actions of developers building momentum toward a viable, sustained mobilization?” The article presents a thematic survey of such actions, including the Quality of Life Movement, exposés of working conditions, gender equity struggles, and unionization efforts. In conclusion, the authors revisit John Kelly’s mobilization theory to assess developers’ capacity to engage in collective mobilization.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.025 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".