Creative economy policies for the video game industry: an Australia/Canada comparison
Bibliographic record
Abstract
The video game industry is a creative industry, however we know little of the policy framework that sustains this sector. In this article we compare two Canadian case studies (Toronto, Montreal) with two Australian case studies (Brisbane, Melbourne). Through the different levels of maturation within these video game hubs we can identify and contrast different policy models. We describe the different case examples as follows: Montreal is a leading video game hub internationally and Toronto is an emerging hub in Canada; Australia is considered as a contender on the international scale, as Melbourne is the leading video game hub in Australia and Brisbane is a former leading hub (recently experiencing decline). Our research is based on interviews with policy advisors at the City and State levels as well as game developers. We also provide an extensive analysis of relevant policy documents to demonstrate the evolution of the policy, specifically the financial incentives made available. The research objective is as follows: To identify the different policy models in place and to evaluate their impact on the growth of the industry for each case study. The main conclusion of this study reveals that Montreal has developed attributes (low cost of labour, availability of skilled labour, quality of university programs, etc.) that none of the other case studies can match; most of which are the direct outcome of policy decision-making.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".