The Geography of Clusters: The Case of the Video Games Clusters in Montreal and in Los Angeles
Bibliographic record
Abstract
The aim of our research was to examine how clusters appear and develop in the video game sector. We thus did a comparative study of the video games cluster in Montreal and Los Angeles. This paper shows that concentration of human creativity in arts and in technology is a significant economic localization factor, but cross-fertilization of sectors and public policy also contributes to the understanding of the emergence of clusters in certain urban regions. Thus, political and industrial factors offer an explanation as to why clusters emerge and how they evolve, going beyond the purely geographic or economic factors. In LA as in Montreal, the cross-fertilization with film is important. However, in Montreal, it is the public policy contributing to financing jobs in the Multimedia City and the French language that brought Ubisoft to the city; this contributed to make the city well known in the field, creating a “brand” for the city and thus fuelling the cluster development.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".