Levels of Complexity: Cultural Diversity, Politics and Digital Games Author #1
Bibliographic record
Abstract
In Europe in the recent past public interest and cultural arguments have been used to achieve exceptions for cultural products from free trade agreements and have led to the development of funding programmes at national and European level to support the production and distribution of certain types of media products. This has been given added impetus by a shift in cultural policy towards ‘cultural diversity’, epitomised by UNESCO’s Declaration of Cultural Diversity. Under pressure from the growth of the Canadian, South Korean and Chinese game development industries policy makers and industry associations in many European countries are starting to consider the cultural role of digital games and funding game production. This trend is epitomized by the French tax credit system for games production and the establishment of funding schemes in France, the UK, Germany and Scandinavia. This paper explores the issue of cultural diversity and digital games and assesses the degree to which you can take a concept, which has a strong legacy in traditional media and national policy regimes, and use it in the context of digital
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".