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Record W1989921463 · doi:10.1300/j514v19n02_06

Body Concepts

2007· article· en· W1989921463 on OpenAlexaff
Lucia F. O’Sullivan, Emma Cooper-Serber, Muriel Kubeka, Abigail Harrison

Bibliographic record

VenueInternational Journal of Sexual Health · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

In a society overwhelmed by HIV/AIDS, a particularly compelling public health question in South Africa is how young people integrate the scientific discourse of HIV/AIDS and pregnancy/contraception with lay beliefs and practices. Our objective was to conduct a preliminary exploration of concepts and related beliefs among young women and men regarding the body, disease and pregnancy. In-depth interviews were conducted with 50 sexually active students recruited from two high schools in a rural region of South Africa. Four primary themes emerged from the interviews. These were (1) sexual activity damages the body, (2) contraceptive use has deleterious effects, (3) menstruation causes physical and moral weakness, and (4) women's bodies are repositories of disease. Their discourses revealed beliefs that sexual activity can have severe physical consequences. A gendered difference emerged through the association of women's bodies with disfiguring, unclean, or immoral behaviors in juxtaposition to men's bodies. Women continue to be linked more closely to concepts of infection than were men. Men assume the role of authorities regarding condom and contraception use in their relationships with women. Findings are discussed in terms of improving young South Africans sexual health, in particular, the health status of women in sexual relationships with men.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.033
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.010
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0330.004

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 head0.063
GPT teacher head0.467
Teacher spread0.404 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations15
Published2007
Admission routes1
Has abstractyes

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