Design Approach for Building Technology with Indigenous Communities
Bibliographic record
Abstract
An increase in demand for mobile platforms in the last decade has led to a widespread need for platform development methods. While these standards do work well for a majority of mobile developers, one audience that can be neglected is the urban Indigenous population of youth in Toronto. Through experience, relationships and an understanding of significant cultural practices and teachings, this study proposes a unique mobile development approach. This approach is tailored specifically towards urban Indigenous youth in Toronto, incorporating the Anishinaabe Medicine Wheel, 7 Grandparent Teachings, and Sharing Circles as main influencers. It also features an experience report of how the mobile development approach worked in practice. Two mobile platforms were built using this approach and achieved successful results, with both becoming popular applications within their respective target audiences. This approach places a focus on the users and essentially aims to have the target audience be the main deciding factor in how the developed platform looks and functions. The motivation behind this study is to make technology less exclusive, and more accessible to a diverse population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".