Disrupting menstrual stigma at work? A thematic analysis of menstrual leave policy announcements across five countries
Bibliographic record
Abstract
Menstrual leave is a policy allowing menstruators to take paid or unpaid time off work if experiencing painful menstrual cycle-related symptoms or illnesses. Scholars have displayed an increased interest in menstrual leave, primarily owing to the rise in companies offering menstrual leave. Efforts have been made to theorise the potential benefits and drawbacks of menstrual leave. Building on prior work, this article conducts a thematic analysis of twelve menstrual leave policy announcements from companies in five countries. Guided by an intersectional feminist theoretical framework, the article uncovers two themes in menstrual leave policy announcements: (1) recognition of menstrual stigma and the potential to normalise menstruation and menstrual health; and (2) the potential for increased worker power. It subsequently develops two critical arguments: (1) menstrual leave may perpetuate (hetero)sexist beliefs and attitudes, and (2) menstrual leave may reify individual responsibility to manage menstruation and facilitate a positive culture around menstrual leave. This research adds to menstruation literature by being one of the few studies to investigate company-level menstrual leave policy announcements in a transnational context. A broader conceptualisation of menstrual leave, including the transition to 'menstrual flexibility' as an umbrella term, could help such policies become equity tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".