The Impact of Mass Game Industry Layoffs on the CHI PLAY Community
Bibliographic record
Abstract
The last two years of 2023 and 2024 has seen massive layoffs in the video game industry. Many games studios laid off a considerable percentage of their work force or closed all together. While the reasons and effects of these layoffs have been extensively discussed, the impact of these layoffs on the CHI PLAY community and the wider field of HCI Games Research has not. Yet, we are deeply intertwined with the game industry, relying on it for the games, players, and communities we study, for the jobs the industry offers our students, and ultimately our funding opportunities. To discuss how these recent layoffs impact our community, if and how we should address these layoffs in our research and teaching and what we as researchers can do to help, we propose to host a panel at CHI PLAY 2024 featuring HCI games researchers, teachers, and industry observers.
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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 | 0.020 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".