Indigenous self-determination in cryospheric science: The Inuit-led Sikumik Qaujimajjuti (“tools to know how the ice is”) program in Inuit Nunangat, Canada
Bibliographic record
Abstract
Inuit have lived along the shoreline of the frozen Arctic Ocean for centuries. Our wellbeing, culture, and identity are closely tied to safe and dependable ice access. As the ice becomes more unpredictable with a changing climate, Elders and experienced ice users recognize that their accumulated wisdom and experience of safe ice travel—their Inuit Qaujimajatuqangit ( IQ ; a term used to describe Inuit knowledge and values) of sea-ice—must be shared and applied in new ways for the benefit of younger generations. Here we illustrate one such application that enables young Inuit scientists to learn and apply the tools and skills they need to create operational community-scale sea-ice maps ( Sikumik Qaujimajjuti , or “tool to know how the ice is”). Our cross-cultural partnership approach—called the Sikumiut-SmartICE model—focuses on developing the skills of young Inuit to create the maps, while non-Indigenous partners provide mentorship, tools, and training. Our novel maps incorporate culturally relevant ice terminology, on-ice monitoring data and observations, and IQ -grounded interpretations of ice features and travel conditions from near-real time optical and radar satellite imagery. The layers of data are integrated into a local GIS, enabling the creation of maps that reflect local and seasonal travel patterns and meet our information needs in information content, extent and frequency. The maps are posted and shared through social media platforms preferred by the community. The maps are a trusted source of travel information because they are made by one of our own, using local language, experience, and IQ . The Sikumik Qaujimajjuti program illustrates the incredible potential of Indigenous self-determination in cryospheric science when the scientific merit of IQ is fully recognized, when Indigenous researchers are able to access technologies and training to apply their IQ , and when non-Indigenous partners mentor and support young Indigenous scientists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".