Bibliographic record
Abstract
There is a recent movement known as Indigenous Futurisms that examines Indigenous perspectives within the context of technology. In relation to this, video gaming continues to be one of the fastest growing forms of new media, yet diversity in the industry is still an issue. There is especially apparent with a lack of proper representation of Indigenous video game characters and Indigenous storylines. While this is starting to change with the recent rise of a handful of Indigenous owned gaming studios and creators, there are still challenges around accessibility for game play. Video games made by Indigenous creators or with Indigenous characters are for the most part non-existent in most public library collections. When we discuss decolonization in libraries, video games as a popular form of media are often overlooked and not viewed as valuable educational tools that encourage literacy and creativity. This paper suggests changes that can be made to ensure that video games that share Indigenous Knowledges and traditions or are made by Indigenous creators are made accessible and are represented in library collections and spaces.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".