Sweetgrass AR: Exploring Augmented Reality as a Resource for Indigenous–Settler Relations
Bibliographic record
Abstract
Augmented reality (AR) is increasingly used as a digital storytelling medium to reveal place-based content, including hidden histories and alternative narratives. In the context of Indigenous–settler relations, AR holds potential to expose and challenge representations of settler colonialism while invoking relational ethics and Indigenous ways of knowing. However, it also threatens to disseminate misinformation and commodify Indigenous Knowledge. Here, we focus on collaborative AR design practices that support critical, reflective, and reciprocal relationship building by teams composed of members from Indigenous and settler communities. After a short history of Indigenous media development in Canada, we describe how we operationalized a participatory AR design process to strengthen Indigenous–settler relations. We document a series of iterative design steps that teams can use to work through ethical, narrative, and technical choices made in the creation of culturally appropriate AR content, and draw attention to the potential and limitations of this emerging medium.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".