Inuit knowledge and use of wood resources on the west coast of Nunavik, Canada
Bibliographic record
Abstract
Driftwood and shrubs are the primary wood resources available in most areas of coastal Nunavik. Today, they are mainly used as fuel for campfires, but historically they were very important for the ancestors of present-day Inuit. This article documents Inuit traditional knowledge about the origin, availability, gathering, and exploitation of wood resources in this region located in the Low Arctic and the Subarctic. Interviews were conducted with 27 Inuit between 60 and 89 years of age in the villages of Ivujivik, Akulivik, Inukjuak, and Umiujaq on the east coast of Hudson Bay. Our data reveal, among other things, that Inuktitut names for pieces of driftwood were based on shape, aspect, colour, and texture. This traditional knowledge was very accurate and highly diverse in the southern villages because of their significant exposure to driftwood. Wood from shrubs (i.e. willows, birches, and alders) was mainly harvested in the fall and used to make fires, mattresses, sleeping mats (alliat), and other objects. According to the participants, driftwood originates in southern Hudson Bay and James Bay and is washed up on the beaches in late summer and the fall. In the far north of Nunavik, where driftwood is small and slender, Inuit used to collect it during the summer from a boat (umiaqorqajaq). Further south, it was gathered during the winter by dogsled.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".