Evaluation of Indigenous garments and Government of Canada cold weather clothing insulation: growing Northern economies through safer personal protective equipment
Bibliographic record
Abstract
The Arctic is one of the harshest environments due to remoteness and freezing temperatures, but Indigenous peoples have successfully persisted there for centuries through the development of protective garments. Limited research suggests that Indigenous-made garments provide excellent insulation. Contributing to this research, the insulation of Indigenous-made garments was measured using a thermal manikin, alongside issued clothing. Complimenting this laboratory work, Indigenous craft producers, Canadian Rangers, Coast Guard Auxiliary, and Government of Canada employees were interviewed and surveyed. It was found that some Indigenous-made garments had similar levels of insulation compared to issued clothing, while others were higher. Interview and surveys highlighted the benefits of Indigenous-made clothing. Interviewees almost universally supplemented their issued clothing to stay warmer, and want to support craft producers through purchasing products. Craft producers prefer using natural and traditional materials due to performance and longevity. Additionally, the act of creating their products has cultural significance with perceived positive mental health benefits. This foundational study demonstrated what Indigenous peoples have known for centuries: garments made with natural materials offer excellent protection, increasing safety in challenging conditions. Procurement of Indigenous-made clothing can increase the opportunities for craft producers, bolster Northern economies, and benefit employees in their work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".