Decolonizing Information Technology Design: A Framework for Integrating Indigenous Knowledge in Design Science Research
Bibliographic record
Abstract
Design science research focuses on the development of artifacts to solve practical problems in our society and there is a strong emphasis on the justificatory knowledge used to support this effort.Kernel theories used as part of the justificatory knowledge have predominantly originated from Western worldviews and resulting artifacts have been developed for modern colonial societies.This approach discriminates against and excludes marginalized groups, including Indigenous Peoples.We draw on the Mi'kmaq guiding principle of Two-Eyed Seeing to explore how Indigenous knowledge can be integrated in design science research as justificatory knowledge.We propose a framework to explain the various paths by which Indigenous knowledge integration can be done and provide examples from the literature for each path.Additionally, we present a case study showing how an Indigenous theory for the design of IT artifacts (prescriptive knowledge) can be applied in the creation of a 3D carronade model.
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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 | 0.034 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.007 | 0.067 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".