Vancouver as Media Cluster: The Cases of Video Games and Film/TV
Bibliographic record
Abstract
If ever Joseph Schumpeter’s (1942) notion of ‘a gale of creative destruction’ has applicability it is to Vancouver, British Columbia. Over the course of the last 25 years, immensely powerful gusts of economic change, cyclonic in their energy, have destroyed Vancouver’s old urban economy based on processing natural resources like lumber and fi sh. In their place, a ‘new economy’ has been erected resting on creative, weightless industries like those in the new media. Consequently, the old inner city where resource activities happened, and where resource- processing workers lived, has been torn up and remade. Even some of Vancouver’s suburbs, particularly the oldest, Burnaby, lying directly to the east of the city, have experienced a radical makeover as former light industrial and warehouse spaces were reconstructed as movie studios and ‘dream factories’ for the manufacture of video games. The purpose of this chapter is to analyse the gale of creative destruction that has touched down in Vancouver, transforming the city into, among other things, a media cluster. In particular, we focus on two of the city’s media industries, both of which generate over a billion Canadian dollars in revenue each year: video games and fi lm and TV production. The chapter is divided into fi ve substantive sections followed by a brief conclusion. First, we outline the recent historical transformation of Vancouver from a staples- producing local metropole to a world city that is now one of the termini on the global media industry’s international circuit of labour, capital and ideas. Secondly, we discuss the origins, development and geography of the video games and fi lm/TV clusters within Vancouver. Thirdly, we describe the particular critical institutional contexts that diff erentially have borne upon and shaped the development of the two clusters, and which include the role of the state, labour organizations and non- profi t institutions. Fourthly, we identify the internal dynamics propelling each of the two industrial clusters, setting their respective future trajectories and constraints. Finally, we speculate about the two industries’ future prospects, which include the blurring of the boundaries that so far have kept them distinct.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".