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Record W2974116087 · doi:10.1177/1469540519876009

Here is a place for you/know your place: Critiquing “biopedagogy” embedded in images of the female body in fitness advertising

2019· article· en· W2974116087 on OpenAlexafffund
Carly Drake, Scott K. Radford

Bibliographic record

VenueJournal of Consumer Culture · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRealmSalience (neuroscience)AdvertisingConsumer CultureIdeal (ethics)SociologyPhysical bodyRecreationAestheticsMedia studiesPsychologyEpistemologyArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

The historically masculine realm of sport has not always been welcoming to women. Today, women have found a place in sport culture, but contemporary media position and address them as objects whose bodies are public goods available for interested parties to judge. In this critical reading of fitness advertisements targeting female recreational endurance runners, we combine poststructuralist feminist theory and a hermeneutic methodology to investigate if and how advertisements participate in this practice. Given the body’s salience at the intersection of sport, the marketplace, and media, we focus on how the body is depicted. We find that advertisements treat the body as a machine, prescribing and normalizing an obsession with athletics. They glorify the pursuit of the ideal running body through athletics and discount women’s potential in and contributions to sport. In this way, advertisements function as a “biopedagogy” that teaches consumers how a suitable body appears and functions.

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)0.005
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.047
Scholarly communication0.0120.009
Open science0.0010.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.347
Teacher spread0.321 · 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

Citations17
Published2019
Admission routes2
Has abstractyes

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