Body techniques of health: Making products and shaping selves in northwest Alaska
Bibliographic record
Abstract
This paper considers the connections between body technologies and wellness. Residents of northwest Alaska suffer disproportionately from social and behavioural illnesses. In Nome and Kotzebue, Inupiat and Yupiit women prescribe traditional activities, such as processing food and making tools and crafts from local harvests, to family members in an effort to promote their well-being. At the same time, Alaska Native institutions organise subsistence activities as a means to generate healthy living among tribal members. This paper seeks to understand why so many Nome and Kotzebue residents view traditional activities as a solution to locally perceived social ills such as substance abuse. The ethnography is based on two groups of women’s collective efforts: processing of seal intoblack meatand learning to make grass baskets—activities locally identified as “traditional” practices. Firstly, this article highlights the body practices developed within spaces of women’s collective production. Secondly, it describes the contemplation and narratives that emerge within these spaces. Lastly, it explores the relationship between body practice and verbal expression, and how this relationship promotes wellness. Analysing Inupiat and Yupiit traditional activities within the framework of technological process reveals how making traditional products also shapes healthy individuals.
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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 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".