Conceptualizing and prototyping an open web-based decolonial methodological platform to facilitate the digitization and equitable management of indigenous knowledge.
Bibliographic record
Abstract
This practice-based PhD study acknowledges that Indigenous peoples continue to experience negative consequences from Western epistemologies. These epistemic frameworks perpetuate the marginalization of Indigenous knowledge systems by shaping data practices that relegate them to the periphery of Canadian society. In response, this dissertation introduces the Decolonial Research Aboriginal Wing (DRAW) platform, a cocreated digital methodological framework developed through long-term, reciprocal engagement with Indigenous First Nation communities and elders. Grounded in graphic design as a methodological and epistemic practice, this study reconceptualizes design as a means of engaging with relational, embodied, and culturally grounded knowledge, not merely as a tool of communication. DRAW is a platform meant for research, knowledge documentation, and governance that contributes directly to the advancement of Indigenous data sovereignty. Rooted in community-based participatory research (CBPR), Indigenous natural law, and relational ethics, the platform invites non-Indigenous researchers to adopt a learner position and make space for Indigenous epistemic authority. DRAW provides opportunities for Indigenous community members, researchers, and the broader society to ethically document traditional knowledge through multimodal features, such as drawing, writing, photography, and audio/video recording. These features support authentic engagement and address the risks of cultural loss through mistranslation or misrepresentation. Every element, including the decolonial database, is intentionally designed to align with Indigenous values and principles. By doing so, DRAW resists the extractive logic of digital and data colonialism by embedding community-determined protocols in the design of decolonial praxis. Through this framework, the dissertation contributes original knowledge to the intersecting fields of decolonial design research, Indigenous data governance, and digital humanities. It reconceptualizes research grounded in Indigenous epistemologies as a site of co-learning, where informed consent is an ongoing, relational process rooted in trust, protocol, and cultural sovereignty. This approach offers a decolonial methodological orientation for ethical, respectful, and reciprocal research practices.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".