Tech Anishinaabe Medicine Wheel: Decolonial Design Principles within Digital Technologies through the Development of the Indigenous Friends Platform
Bibliographic record
Abstract
Digital technologies are not only colonial in their practices, but they are colonially created and designed. Despite the implementation of worldwide responses to counteract the effects of digital coloniality, there is still an absence of decolonial and Indigenous ways of doing digital technologies. The objective of this dissertation, therefore, is to formulate design principles of decoloniality within digital technologies through the story of the development of the Indigenous Friends Platform (IFP) in the context of Indigenous urban youth at York University in Tkaronto, Canada. The storytelling of the Indigenous Friends Platform describes how in the context of Indigenous youth in Tkaronto, the decolonial design of an Indigenous mobile application needed to be explored through a process of doing through thinking, thinking through doing. In that process of development and reflection, the mobile application was conceived as a technical being who has a Spirit and founded a tech-community: the Indigenous Friends Association. This technical being was developed in four stages that help to differentiate this space from other mainstream hegemonic digital applications and to sustain this technological solution in the long term. These four transdisciplinary stages frame the Tech Anishinaabe Medicine Wheel that consists of four design principles of decoloniality within digital technologies: (1) Waabinong (East) Digital Software Braid; (2) Zhaawanong (South) Embodiment of Indigeneity; (3) Epangishmok (West) Decolonial Infrastructure; and (4) Kiiwedinong (North) Indigenous Data Sovereignty. These four design principles foster the theoretical reflections of decoloniality and digital technologies through the differentiation of digital decoloniality and decolonial computing. Moreover, these principles provide digital activists and Indigenous communities several insights into how digital technologies can be decolonially implemented and reimagined at the community level.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".