“When We’re on the Ice, All We Have is Our Inuit Qaujimajatuqangit”: Mobilizing Inuit Knowledge as a Sea Ice Safety Adaptation Strategy in Mittimatalik, Nunavut
Bibliographic record
Abstract
Increased variability in weather and sea ice conditions due to climate change has led to high rates of injury, trauma, and death for Inuit travelling on the sea ice. Contributing to these high rates are the ongoing effects of colonial policies that diminish and disrupt the intergenerational transfer of sea ice Inuit Qaujimajatuqangit (IQ). Despite these challenges, place-based experiential IQ continues to be the most important information source for safe travel on the sea ice. This paper presents an Inuit-led, coproduced, cross-cultural research project in which Inuit youth documented and mobilized sea ice IQ in Mittimatalik (Pond Inlet), Nunavut for safe community sea ice travel. We outline the Inuit youth training to facilitate the terminology and participatory mapping workshops and to document this IQ. We also discuss the IQ that was most important to share, and the mapping and artistic methods used to mobilize this IQ into a booklet, maps, and posters. Inuktitut sea ice terms are the foundation to enable youth with the skills to learn about sea ice IQ with experienced hunters. IQ enables Inuit to interpret and synthesize information from weather forecasts, earth observations, and community-based monitoring to apply to local conditions. Seasonal IQ maps of safe and hazardous sea ice conditions provide travel planning information at spatial and temporal scales that supplemental information sources cannot address. The IQ products mobilize preparedness, situational awareness, navigation, and interpretation skills so Inuit youth can become more self-reliant, as access to technology is not always possible once out on the sea ice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".