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Record W3113239325 · doi:10.1163/15734218-12341464

To Do Nothing

2020· article· en· W3113239325 on OpenAlexaff
Victoria Sheldon

Bibliographic record

VenueAsian Medicine · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNothingEnvironmental ethicsCraftFeelingSociologyAestheticsIndependence (probability theory)Gender studiesHistoryCriminologyPsychologySocial psychologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract In Kerala, South India, individual pursuits of nature cure ( prakr̥ti cikitsa ) invoke ethical narratives about an idealized purer past, contrasting a dangerous present saturated with social and environmental toxins. While first popularized in India by M. K. Gandhi, nature cure has gained contemporary fame as a low-cost intervention for Kerala’s purported health crisis: chronic lifestyle diseases. Nonprofessionalized natural healers identify as public health activists, teaching predominantly urban, middle-class patients how to revive local lifeways of self-doctorhood. This article narrates how two aging patients internalize their naturopathic doctors’ advice to detoxify and “do nothing” rather than strive for biomedical cure. By naturally revitalizing their bodies, they cultivate feelings of intense independence and ecological attachment that reconfigure experiences of migrated-kin isolation. In counterpoint to literature that frames biopolitical and medical discourses as causally producing moral subjectivities, this article demonstrates how persons agentively craft counternormative, vitalistic models of aging and health, contributing to broader localist imaginaries of reviving pre-toxic lifeways.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.054
GPT teacher head0.360
Teacher spread0.306 · 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 designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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