Videogame developers among “extreme” workers: Are death marches over?
Bibliographic record
Abstract
Purpose: The videogame industry is a work environment that is emblematic of O’Carroll’s (2015) encompassing model of a 24/7/365 working time model of flexibility. We use O’Carroll’s model to challenge two myths about videogame developers (VGDs): the long hours of work are in fact unpredictable hours, and flextime HR programs do not allow for real control over working hours.\nDesign/methodology/approach: We use a mixed methods approach (international online survey and 100 Canadian interviews) to analyse the case of VGDs - a different, but similar type of worker to the IT workers analysed by O’Carroll.\nFindings: We can generalize O’Carroll’s model based on the IT case to the VGD case. Based on these two cases, we propose that the rise of project-based work environments is a major explanatory factor of this raising trend in the 24/7/365 model of flexibility.\nResearch limitations/implications: More research examining project based regimes in other sectors and settings is required to generalize further.\nOriginality/value: Though this model can appear to fit the reality of knowledge work in general, it more accurately describes project-based work in creative environments, which is nearly always knowledge work, but the reverse cannot be inferred.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".