Storied Indigeneity in Videogames: Post-Indian Warriors and Indie Japan
Bibliographic record
Abstract
By drawing a line between seemingly two disparate phenomena, the introduction of story elements into videogames by Japanese developers working during the latter part of the last century and the current efforts of Indigenous game developers to tell stories with videogames in the current one, this thesis links the productive practices of two different groups: independent game developers in Japan, and high school students on the Kahnawake Mohawk reserve outside Montreal. Practices are placed in the broader context of storied creators working during the ‘Golden Era’ of the Japanese games industry in the 1980’s, and North American developers working, with the help of Indigenous consultants, to develop AAA games featuring Indigenous content in the 2010’s. By first looking at the formal innovations in storytelling that the Japanese games industry brought to the medium and then, through analysis of a series of interviews with independent game developers working in that same industry, this thesis critically considers how independent developers in Japan develop games. Finally, by reviewing three of examples of Indigenous game development in North America, this thesis discusses how the practices might be transferable.
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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 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".