Quality of Life in the Game Industry. Report of the Quality of Life survey 2009
Bibliographic record
Abstract
This report commissionned by the International Game Developers Association (IGDA) disseminates the outcome of the Quality of life survey (QoL), which remains a partnership with Western University and TÉLUQ. The QoL was open to anyone involved in the video game industry in a professional or academic capacity. IGDA was looking for a systematic way to understand game developers worldwide, including both IGDA members and non-members, knowing developers’ priorities and the most pertinent issues affecting their overall satisfaction. These insights will be leveraged to help prioritize the IGDA’s advocacy efforts and initiatives. In 2004, the IGDA launched its initial Quality of Life survey in an effort to gain a much clearer understanding of the issues that affect life as a game developer – from “crunch time” to compensation issues. In 2009, the IGDA repeated the Quality of Life survey in partnership with researchers at Western University in Ontario, Canada and TÉLUQ in Québec, Canada. The survey once again provided more insights into how the issue was evolving in our industry, and then a few years ago the IGDA conducted a separate diversity survey to help us obtain a clearer perception of developer demographics. In 2014, IGDA launched a third survey (report of which is also available in this archive) called the Developer Satisfaction Survey (DSS), which remains a partnership with Western University and TÉLUQ, as well as new partners M2 Research and the Georgia Institute of Technology.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".