Mapping Traditional Knowledge: Digital Cartography in the Canadian North
Bibliographic record
Abstract
Digital cartography offers exciting opportunities for recording indigenous knowledge, particularly in contexts where a people's relationship to the land has high cultural significance. Canada's north offers a useful case study of both the opportunities and challenges of such projects. Through the Geomatics and Cartographic Research Centre (GCRC), Inuit peoples have been invited to become partners in innovative digital mapping projects, including creating atlases of traditional place names, recording the patterns and movement of sea ice, and recording previously uncharted and often shifting traditional routes over ice and tundra. Such projects have generated interest in local communities because of their potential to record and preserve traditional knowledge and because they offer an attractive visual and multimedia interface that can address linguistic and cultural concerns. But given corporations' growing interest in the natural resources of the Arctic and the concomitant rise in government concern about claims to Arctic sovereignty, such maps may also be of interest to a broad range of actors and for a variety of purposes. Because these projects rely heavily upon, and record, oral knowledge, and because they convert such knowledge into highly malleable and easily disseminated digital content, they raise challenging issues around informed consent, intellectual and cultural property, and privacy. This article identifies and examines these issues and describes the collaborative and interdisciplinary research established to identify and address the use of traditional knowledge in digital cartography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".