From felt tip to technology: the challenges of representing traditional knowledge in a GIS platform to create a knowledge surface
Bibliographic record
Abstract
Traditional knowledge (TK) has been the keystone to survival in the Arctic for thousands of years. Caribou are integral to the society, health and culture of the Inuit, the Indigenous peoples of the Arctic. There is a lack of research regarding caribou on King William Island (KWI), Nunavut. Through a project in Gjoa Haven, located on KWI, Inuit Elders and hunters used maps to help represent their knowledge of caribou in the region. These 32 maps were processed in a GIS to explore the spatial dimensions of TK, and different forms of knowledge representation. Using vector data the features drawn were separated into lines and polygons to show hotspots of caribou knowledge. Using a fuzzy raster methodology, all caribou data was summed to create a collective knowledge surface of the caribou features. These maps refine the data from the vector maps and create a continuous surface that aims to better reflect the collective nature of TK. This research explores the challenges of representing TK using western technologies, and application of fuzzy methodologies for improving the representation.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".