Étude comparative des politiques publiques de soutien au secteur des jeux vidéo
Bibliographic record
Abstract
In recent decades, video games have become one of the major engines of growth in the creative industries and one of the most important and dynamic segments of the media and entertainment sector. In 2009, video gaming’s global revenue was US$ 52 billion; now estimates anticipate a rise from US$227 billion in 2023 to US$ 327 billion in 2028. The industry has been widening its audience to include older and more female users. Worldwide, there were over three billion players at the end of 2022. It took three decades for the entertainment video industry to be recognised as a major industry. Then the sector has been progressively acknowledged as an economic and cultural asset. The public authorities of many countries started developing specific support policies. Often these policies depended on the interventionist (or non-interventionist) traditions of the countries involved, as well as on the initial situation in each case. Even if the interventions are ultimately about supporting the industry, the hierarchy of goals differs. This article offers a comparative study of public policies supporting the video game industry. It seeks to highlight the diverse motivations behind these policies, including economic development, support for technological innovation, assistance with creation, heritage development, and the promotion of cultural heritage. The article focuses on four countries (Canada, China, North Korea, and France) to show how these policies, which may appear similar, are primarily the product of local, national, and regional histories and contexts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".