Bibliographic record
Abstract
The notion of scenes has helped frame how particular clusters of cultural activities, practices, and "happenings" simultaneously replicate and transform global practices in specific localities. The study of scenes has aided us in examinations of how geographic and virtual localities create and shape global industries, movements, and genres. In this article, I focus on the Toronto game production scene to examine how it replicates and transforms the wider cultural norms, working conditions, and genre productions of the global game industry. Based on a two-year ethnography of the scene, I survey how gamemakers maintain and challenge the expected norms and practices of industry and platforms in the production of local games. To identify these clusters of cultural activity, I develop the notion of scenes as palimpsests to trace how gamemakers replicate and transform industry cultural norms and practices in the local scene. The last decade has seen the emergence of social media platforms as a venue for participants of scenes to discuss, create, and disseminate their works with geographically local and global audiences. The textual spaces of these platforms connect participants of local production scenes to a global community defined by geography, industry, and genre. By tracing scenes through its inscriptions, I examine how these platforms are centers for encounters between the values and practices of the Toronto game production scene and the wider industry. This article is about how the geographical cultural activities of scenes are shifting into virtual environments, and how these virtual spaces are transforming the cultural norms and practices of gamemaking and its associated activities, such as socials, game jams, and "talking shop." I argue that analyses of globalization must consider the wider physical and virtual infrastructures of local production to understand how cultural media are produced and circulated around the globe.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".