When a door becomes a window: using Glassdoor to examine game industry work cultures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite a growing interest in its labour conditions, research about the games industry remains constrained by the streetlight effect. Non-disclosure and non-disparagement agreements, or HR blocking access for embedded research, are examples of how the industry can shield itself from outside view. Furthermore, when access is granted, researchers might find themselves constrained by their own timelines (tenure clocks, graduation deadlines, etc.) that are antithetical to long-term, embedded ethnographic research. In this article, I discuss an opportunity to look behind the curtain of the game industry via employee reviews left on Glassdoor, a popular job-seeking website. These reviews provide a means to gather worker perspectives in a way that reduces the potential for harm for those who speak out to counter the dominant, PR-polished narratives about working in the game industry being a ‘dream job.’ To demonstrate Glassdoor’s utility as a supplemental data source for investigations of industry workplace cultures, I draw on employee reviews describing their experiences at Riot Games, developer of the Multiplayer Online Battle Arena League of Legends. These first-hand accounts of working at Riot provide a view into the games industry, allowing observation of how problematic work cultures become normalized, and ultimately, how workers who do not come to internalize these norms may be pushed out.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it