All kinds of seasons: articulating Labrador Inuit governance through crafting a seasonal calendar
Bibliographic record
Abstract
Inuit have always worked within seasonal patterns, using ecological observations to make predictions about weather, ocean and ice conditions, species presence, and environmental change. Monitoring, intergenerational knowledge sharing, and strong relationships have supported Inuit as they have responded to environmental conditions through generations. This has informed a unique understanding of seasonal change that is reflective of Inuit knowledge and relationships to land, water, and ice. Calendars across Inuit Nunangat are unique to the social–ecological regions in which they were developed. In this collaboration between Inuit and non-Inuit researchers, artists, and knowledge holders, we developed a seasonal calendar for Nunatsiavut that is representative not only of activities and species harvested throughout the year, but that also depicts a Labrador Inuit relationship to seasonality. This research is part of the Imappivut Knowledge Study, a participatory research process designed by the Nunatsiavut Government to inform spatial planning in the marine environment of northern Labrador. The resulting calendar expresses an Inuit experience of the seasons as they are inexorably linked to life and wellbeing in Nunatsiavut. The calendar can be used to reframe Federal and Provincial environmental management policies, such as harvest regulations for Atlantic salmon, to align with Nunatsiavut-based indicators of seasonal change.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".