Gaming on the edge: Mobile labour and global talent in Atlantic Canada's video game industry
Bibliographic record
Abstract
Diminishing returns and advances in telecommunications have prompted large video game firms to seek new locations, outsource production, and develop niche studios, including on Canada's East Coast. In this paper, we examine emerging occupational cultures and trace the origins and evolution of video game production in Canada's Atlantic provinces—a critical yet peripheral space economy in the gaming sector. Our findings are drawn from 30 interviews with gameworkers, studio managers, government officials, and other industry experts. We find this industry to be driven by the confluence of three major factors: (i) provincial governments have supported video game development as a strategic industry via financial incentives; (ii) firms are benefiting from a return migration effect and are repatriating Atlantic Canadian talent from media hubs by selling “home,” work‐life balance, and an alternative to the punishing gamework culture associated with Silicon Valley; and (iii) post‐secondary institutions in the region have improved their talent pipelines through computer science, digital media, and video game development programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".