Bibliographic record
Abstract
Feminist scholars, activists, and artists have long addressed the topic of virginity and have dismantled it as a powerful, globally circulating, and gendered myth. It affects how many woman-identifying people experience how their bodies become (a)sexual. Centrally, the myth of virginity has been shown to be mobilized in support of colonial, ethnonationalist identity projects. In Kyrgyzstan, disciplining women through policing their sexual behavior co-constituted nation-building projects after the dissolution of the Soviet Union. Drawing on data collected since 2017 (qualitative interviews in Bishkek and Osh and qualitative research on Instagram), I examine intimate geographies of the virginity myth in Kyrgyzstan. Building on geographic scholarship on intimacy and body parts, I discuss the ways in which virginal blood works both to submit to and to reclaim one's intimate body spaces and sexual practices. I argue that people affected by the virginity myth create the foundations for intimate justice in Kyrgyzstan. On the one hand, people are reclaiming authority over their intimate bodies through subverting sexist systems of sexual control and through expanding discursive horizons of virginity performances. On the other hand, activists on Instagram are supporting digital public spaces that allow to author virginal blood narratives and provide resources on intimate body knowledge. Analyzing the scalar doings of virginal blood, the paper contributes a case study on intimate justice and the geographies of the body in Kyrgyzstan. My analysis encourages further examination of the capacities of the body to better understand how certain body parts turn into key sites of (geo)political struggles and can transform them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".