The Igliniit project: Inuit hunters document life on the trail to map and monitor arctic change
Bibliographic record
Abstract
The Igliniit Project brought together Inuit hunters and geomatics engineering students during the International Polar Year (IPY) to collaborate on the development and testing of a new integrated GPS/PDA/mobile weather station technology for observing and monitoring the environment. Part of the larger Inuit Sea Ice Use and Occupancy Project (ISIUOP), the Igliniit Project culminated in a tangible product that is the direct result of combined scientific and Inuit knowledge, ingenuity, and engineering. This paper describes the Igliniit Project and examines the resulting technology as (i) an artifact of Inuit knowledge, science and engineering collaboration; (ii) a tool for meaningful engagement of Inuit in environmental science and community‐based monitoring; (iii) a new approach and tool in the field of indigenous mapping; and (iv) an example of one technology in the expanding ecology of technologies in everyday Inuit life. The technology requires improvements in hardware and further development of supporting systems such as data management and mapping capability, but there is potential for the Igliniit Project approach and system to have wide appeal across the North for a variety of applications including environmental monitoring, wildlife studies, land use mapping, hazards research, place names research, archaeological and cultural inventories, and search and rescue operations .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".