Storying Design Practice with Five Indigenous Design Paradigms
Bibliographic record
Abstract
How can design teaching and the design professions decolonize design practice and create a reciprocal praxis for both Indigenous and Non-Indigenous practitioners, students, and scholars? This is a question, as both Indigenous and settler scholars, we have begun to address through the development of a new methodological praxis with guiding paradigms. By embracing the concept of Anishinaabemowen, Gimiigiwemin, “we are exchanging gifts,” we prioritize listening to the land to ensure that our process considers place more thoroughly. This process-based design approach is intended to contribute to help guide both academic and professional design practices to engage more meaningfully with an expanded world view that prioritizes creating meaningful connections to the land. The Five Decolonizing Design Paradigms are rooted in land-based teaching initiatives through the Faculty of Architecture. These paradigms are ever developing, and are shared here through stories of projects that are guided through the teachings of: Danakamigad: it takes place, happens in a certain place; Andotan: listen for it and wait to hear it; Bawaajigan; a dream, a vision; Meshkwad: in turn, in exchange; and Naagotoon: make it show, reveal it. The intention of these paradigms and these experiences is that they may contribute to a path forward for the design disciplines as we collectively work towards truth and reconciliation.
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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 | 0.028 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.061 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".