A recognized need for growing Northern economies and safer personal protective equipment for Government of Canada employees: research for the National Research Council of Canada’s Indigenous clothing ensemble project
Bibliographic record
Abstract
This research examines the importance of Indigenous Knowledge for outdoor clothing in the Arctic. Government of Canada (GoC) employees are issued clothing such as Gore-Tex outerwear, boots, rainwear, underpants, cargo pants, and more as Personal Protective Equipment (PPE) to keep them warm while working in the North. These employees feel as though their issued clothing could be improved by the addition of Indigenous Knowledge, through incorporation of Indigenous-made clothing with their PPE. Through interviews with Indigenous craft producers in the Arctic, volunteers with the Canadian Rangers and the Coast Guard Auxiliary, as well as a survey to GoC employees, this project examined personal preferences for PPE, and construction, care and longevity for Indigenous-made clothing. This was carried out to see if clothing handmade by northern Indigenous craft producers provided superior protection in harsh weather, from personal experience with the clothing, versus issued / commercially-available clothing. This research argues that by the purchasing of Indigenous clothing by Canadian Government departments, Northern economies will be bolstered, GoC employees can be better supported in their work and the economies for artists and craft producers in the North can also be improved. This, in turn, could improve the quality of life for northern communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".