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Record W2943785595 · doi:10.1111/cag.12522

Gaming on the edge: Mobile labour and global talent in Atlantic Canada's video game industry

2019· article· en· W2943785595 on OpenAlexaffvenueabout
Yolande Pottie‐Sherman, Nicholas Lynch

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVideo gameStudioBusinessIncentiveOutsourcingGovernment (linguistics)MarketingWork (physics)EconomicsMarket economyEngineeringTelecommunicationsMultimedia

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.210
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2019
Admission routes3
Has abstractyes

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