Ancestral code : grounding Indigenous computing in nehiyaw epistemology
Bibliographic record
Abstract
This dissertation explores the intersection of Indigenous knowledge and modern computing practices. The author uses a nehiyaw (Plains Cree) perspective to develop innovative software and hardware designs that incorporate Indigenous cultural values and epistemologies. The research begins with an examination of the historical context of computing and establishes a theoretical framework rooted in nehiyaw storytelling and epistemology. It then explores the development of Indigenous-informed software, including a syllabic programming typeface, an integrated development environment, and a nehiyaw-based programming language. The dissertation also explores Indigenous hardware design, focusing on keyboard arrangements for Romanized nehiyawewin as well as the creation of a syllabic keyboard based on the nehiyaw syllabic star chart. Tying together both of these areas of computing – software and hardware, the author proposes an Indigenous computing framework that aims to bridge the gap between Indigenous culture and modern digital practices. This framework opens up new avenues for research and offers a unique perspective on the future of computing. The dissertation concludes by summarizing the key findings, discussing the implications of the research, and outlining potential future directions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".