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Record W2167899614 · doi:10.1123/ssj.29.3.325

The Gap Between Knowing and Doing: How Canadians Understand Physical Activity as a Health Risk Management Strategy

2012· article· en· W2167899614 on OpenAlexafffundabout
Christine Dallaire, Louise Lemyre, Daniel Krewski, Laura Beth Gibbs

Bibliographic record

VenueSociology of Sport Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsUniversity of Ottawa
FundersHealth Canada
KeywordsMantraPleasurePhysical activityPsychologySociologyActive livingPhysical healthSocial psychologyGender studiesMedicineMental healthPhysical therapyPsychotherapist

Abstract

fetched live from OpenAlex

In Canada, as in other neo-liberal states, a physically active lifestyle is discursively constructed as a moral activity, whereas a sedentary lifestyle is criticized as a failure to take charge of one’s health (Bercovitz, 2000; Lupton, 1997). This study aims to understand how Canadian men and women articulate the discursive connections between physical activity and health risks and how those connections are reflected in their reported behaviors. Analysis shows that some of the 37 men and 36 women interviewed not only “talk the talk” regarding physical activity, they also claim to lead an active lifestyle. However, “active” participants were disciplined into frequent physical activity not simply by the discursive effects of the fitness mantra promising better health, but because they enjoyed it. Conversely, the not-active-enough participants were unwilling to fully comply with the requirements of the fitness discourses because they found no pleasure in “exercise.” Despite adopting physical activity as a key strategy to manage their health risks, interviews revealed that the latter group were not docile bodies (Foucault, 1995).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.666

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0430.030
Scholarly communication0.0130.005
Open science0.0030.005
Research integrity0.0040.006
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 head0.060
GPT teacher head0.362
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2012
Admission routes3
Has abstractyes

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