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Record W2804242571 · doi:10.5744/rhm.2018.1009

The Self-Generating Language of Wellness and Natural Health

2018· article· en· W2804242571 on OpenAlexaff
Colleen Derkatch

Bibliographic record

VenueRhetoric of Health & Medicine · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNatural (archaeology)Rhetorical questionRhetoricIdeal (ethics)AutopoiesisSociologyEpistemologyPsychologySocial psychologyLinguisticsPhilosophyHistory

Abstract

fetched live from OpenAlex

This article extends Keränen’s (2010) application of the concept of autopoiesis, or self-generation, to rhetoric by examining how arguments about wellness and natural health self-generate in public discourse. The article analyzes 20 qualitative interviews on what it means in contemporary culture to be “well”—how wellness differs from illness, how it is distinct from health, and how it can be maintained and enhanced. The analysis shows that wellness discourse is predicated on the entanglement of seemingly opposed logics of restoration and enhancement: those who seek wellness through dietary supplements and natural health products seek simultaneously to restore their bodies, perceived as malfunctioning, to prior states of ideal health and well-being, and to enhance their bodies by optimizing bodily processes to be “better than well” (Elliott, 2003). The fusing of these two logics creates an essentially closed rhetorical system in which wellness is always a moving target.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.456
Teacher spread0.415 · 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

Citations38
Published2018
Admission routes1
Has abstractyes

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