Indigenous Knowledges as Justificatory Knowledge in Design Science Research: An Expository Case
Bibliographic record
Abstract
In developing information technology (IT) artifacts to solve practical problems in society, design science research places a strong emphasis on the justificatory knowledge that informs their design. Historically, justificatory knowledge has privileged Western worldviews and scientific approaches, resulting in IT artifacts that discriminate against and exclude marginalized groups. As Indigenous peoples reclaim lost rights and seek to establish digital sovereignty, there is the need to understand and elevate the value of Indigenous knowledges within the design science paradigm. Building on the Indigenous Knowledge Integration Framework, this article discusses how Indigenous knowledges can directly inform IT design and demonstrates this potential using an expository case where Māori tribal protocols for pōwhiri (welcoming visitors) are used to structure the development of a large language model (LLM). The development of the LLM confirms that Indigenous knowledges can enhance the construction of IT artifacts. The contributions of the article lie in showing how Indigenous knowledges can be applied ex ante as justificatory knowledge, demonstrating how Indigenous knowledges intrinsically linked with minority languages can support the design and development of more inclusive LLMs, and charting a way toward more inclusive design science research and digital sovereignty for Indigenous and other marginalized peoples.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.044 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".