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Record W2113571675 · doi:10.7202/1003911ar

Body techniques of health: Making products and shaping selves in northwest Alaska

2011· article· en· W2113571675 on OpenAlexvenueno aff
Amber Lincoln

Bibliographic record

VenueÉtudes/Inuit/Studies · 2011
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSubsistence agricultureNarrativeEthnographyGastronomySociologyPublic relationsEnvironmental ethicsPolitical scienceGeographyAnthropologyLawAgriculture

Abstract

fetched live from OpenAlex

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 into black meat and 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.213
GPT teacher head0.435
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2011
Admission routes1
Has abstractyes

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