Bibliographic record
Abstract
“The sexualization of menstruation perpetuates harmful stereotypes and undermines gender equality. This study examines its intersections with rape culture, commodification, and gender perceptions. Using feminist theories and discourse analysis, it investigates how menstruation is sexualized through themes of rape, tampons, and derogatory labels. Commercialization reinforces this notion, contributing to gender inequality and stifling discourse. By dismantling these narratives, society can promote gender equality and foster inclusivity. Menstruation is a pervasive yet often overlooked aspect of gender-based discrimination. This research explores its societal implications, shedding light on the objectification and exploitation that occur. The study analyzes narratives and languages contributing to this phenomenon, highlighting the normalization of objectification through rape culture. Additionally, it examines the commercialization of menstruation, particularly within the tampon industry, and its inadvertent reinforcement of shame and sexualization. The derogatory label ‘prostitutes’ is also scrutinized for its role in demeaning women and equating their worth with reproductive functions. The implications of sexualizing menstruation are dire, including perpetuating gender inequality and infringing upon bodily autonomy. By understanding these intersections, society can engage in larger conversations surrounding gender-based violence and discrimination. This research calls for a collective effort to challenge societal narratives and debunk the sexualization of menstruation, ultimately promoting gender equality and creating a more inclusive environment for all.”
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 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".