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Record W3080090231 · doi:10.1177/0959353520944144

The clean vagina, the healthy vagina, and the dirty vagina: Exploring women’s portrayals of the vagina in relation to vaginal cleansing product use

2020· article· en· W3080090231 on OpenAlexaff
Amanda J. Jenkins, Kieran C. O’Doherty

Bibliographic record

VenueFeminism & Psychology · 2020
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVaginaBacterial vaginosisIntravaginal administrationMedicineGynecologySurgery

Abstract

fetched live from OpenAlex

Vaginal cleansing products such as douches, sprays, wipes, powders, washes, and deodorants are part of a growing $2 billion industry in North America. Part of the appeal of these products is supposedly attaining vaginal cleanliness, which is marketed in association with product use. Although these products are promoted as healthy, medical research indicates potential health risks for some of these products (e.g. yeast infections, bacterial vaginosis, and disruption of the vaginal microbiota). Despite these risks, many women use these products. In this paper, we draw on interviews with women who use vaginal cleansing products to examine the ways in which particular portrayals of the vagina are connected with broader societal messages about female genitalia and with motivations to use vaginal cleansing practices. These portrayals include the healthy vagina, the clean vagina, and the dirty vagina. We show that although participants in our study valued both a clean vagina and a healthy vagina, when tension occurred between these two portrayals, participants prioritized vaginal cleanliness over vaginal health. We argue that this prioritization of the idealized clean vagina is connected to societal pressures of needing to attain unrealistic standards of vaginal cleanliness.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.076
GPT teacher head0.325
Teacher spread0.249 · 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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

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