Video Games Beyond Play. Decolonizing Gamevironments
Bibliographic record
Abstract
The true impact of videogaming is far from being fully comprehended and properly evaluated in our hypermediated world. Through theory and exploration, our scholarship examining videogames have shown us the proverbial tip of the iceberg when it comes to unpacking the cultural and social impacts linked with this significant form of media. Videogame research demonstrates that the games we play often reflect our cultural value systems and reinforce narratives and themes linked with the dominant values and ideologies of society. However, research has also illustrated that video games provide opportunities for counter narratives that challenge the dominant ideology, including colonization, and present alternative ways of being. In this way, videogames have become a site of resistance against colonial structures and a source of hope for decolonizing societies. By expanding the focus of videogame studies beyond research that focuses upon the game, the game producers, and the game players and instead considering the gaming context within our current society, the gaming-related actants (including the non-human) and gaming-related media practices, the larger cultural impact of video games can be better evaluated beyond just play. Through this new gamevironments lens, it can be argued that videogames may be one of the most significant forms of new media for supporting decolonization and changing cultural perceptions about Indigenous identities and ways of being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".