Mapping Inuinnaqtun: The Role of Digital Technology in the Revival of Traditional Inuit Knowledge Ecosystems
Bibliographic record
Abstract
The term Inuinnaqtun is often used in reference to a dialect of Inuktut spoken by Inuinnait (Copper Inuit) of the Central Canadian Arctic. The broader meaning of Inuinnaqtun, however, is to speak, to create, to practice, to do, to think, to be, like an Inuinnaq (a human being). Inuinnaqtun was once its own robust ecosystem, with Inuinnait physically immersed in a landscape and way of life that nourished a fluent and full language, supported human relationships, and maintained a sophisticated body of cultural knowledge. The Inuinnait journey into the 21st century has challenged the practice of Inuinnaqtun, along with the connectivity of its ecosystem. How can an integrated Inuinnaqtun ecosystem be restored in contemporary Inuinnait society? In this paper, we outline the decade-long development of a digital mapping program to document traditional forms of engagement between Inuinnait people, language and land, and facilitate the continued circulation of knowledge that underlies these relationships. In reviewing its various successes and challenges, we critically question digital technology’s ability to digitally represent Inuinnaqtun ontology, in addition to the role that digital technologies can play in facilitating the local relocation of knowledge, objects and relationships dispersed into global contexts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".