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Record W1584149861

Smear It on Your Face, Rub It on Your Body, It’s Time to Start a Menstrual Party!

2010· article· en· W1584149861 on OpenAlexaff
Shannon Docherty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsWestern University
Fundersnot available
KeywordsMenstruationFeminismMainstreamGender studiesPsychologySociologyMedicinePolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Abstract: This article explores current attitudes about menstruation and the resulting menarchy movement. Menarchy, or menstrual anarchy, is a response to negative attitudes about menstrua-tion. Menarchists critique the femcare industry, pharmaceutical companies, and advertisements that produce and reinforce ideas that menstruation should be concealed and hidden. Feminist theorists reference a long history of equating menstruation with failed reproduction and reduc-ing menstruation to a curse. The commodification of menstruation and women’s bodies com-bined with bioethical implications of menstrual suppression have created a sense of urgency in the menarchist movement. Menarchists, influenced by Third Wave feminism and the Do-It-Yourself-inspired punk counterculture, are coming out of the menstrual closet. As activists and artists, they are creating alternative menstrual products and critiquing mainstream discourses about menstruation. This article exposes part of the expanding menarchist archive that is ac-cumulating on the Internet. Menarchists are critiquing and improving menstrual management while simultaneously reconceptualizing menstruation. By embracing the abject quality of men-strual blood, menstruators are transforming their own attitudes toward their monthly cycle and radicalizing menstruation.

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)0.001
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 score0.065
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0650.015

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 head0.065
GPT teacher head0.268
Teacher spread0.203 · 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

Citations11
Published2010
Admission routes1
Has abstractyes

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