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Record W3126346939 · doi:10.1177/1363459320988886

The piety of optimization: The rhetoric of health awareness in ParticipACTION and Fitbit

2021· article· en· W3126346939 on OpenAlexafffund
Loren Gaudet

Bibliographic record

VenueHealth An Interdisciplinary Journal for the Social Study of Health Illness and Medicine · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPietyRhetoricSociologyRhetorical questionPublic relationsPower (physics)Public healthCorporationSet (abstract data type)AestheticsMedicinePolitical scienceComputer scienceNursingLawPhilosophy

Abstract

fetched live from OpenAlex

This article uses the tools of rhetorical study to investigate how health awareness, as both a concept and a set of beliefs that reinforce ideals of health, permeates everyday life and affects ways of being. I explore how health awareness is communicated through both public health and commercial marketing campaigns, and argue that as the sources of information change, so too do the ideas of health that we are asked to be aware of. Through an analysis of the websites of ParticipACTION, a publicly funded health and fitness campaign, and Fitbit, a corporation that produces wearable technologies, I show that these organizations provide their audiences with instructions for self-conduct in the pursuit of health through the piety that time is a resource to be managed. Through this piety, ParticipACTION and Fitbit's websites each reify an altar of health where health is represented as a socially and physically fitter (optimized) self, always just out of reach and attainable in the future. I conclude with a call for critical descriptions of health awareness to move beyond the explanatory power of neoliberalization of health, and turn to the work of Rachel Sanders, Annmarie Mol, and Donna Haraway as possible avenues for resisting optimization.

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)0.004
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 score0.467
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0120.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.149
GPT teacher head0.456
Teacher spread0.307 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueHealth An Interdisciplinary Journal for the Social Study of Health Illness and MedicineSame topicRhetoric and Communication StudiesFrench-language works237,207