Privacy is a feminist issue: Reconsidering data sharing in menstrual self-tracking apps
Bibliographic record
Abstract
Though data governance, regulation and privacy are thought to be at the centre of a just society [1], feminist scholars have pointed out that privacy has historically been “a right not granted equally to all” [2, p. 1450]. Indeed, recent work has sought to identify how marginalized groups are especially prone to invasions of privacy, including having their private information used without their knowledge or consent in ways that can sustain and/or exacerbate social inequities [3],[4],[5]. In this presentation, I explore how the unethical data sharing practices of menstrual self-tracking apps like Flo and Ovia contribute to these problems. Flo prods users to log sensitive health data with explicit promises to “keep personal information private and secure” [6], though the Wall Street Journal demonstrated in 2019 that the company secretly shared user data with corporations for years [7]. Similarly, Ovia shared data with users' employers [8]. The consequences of these actions are complex, ranging from undermining users' health and well-being to potentially contributing to gender gaps in employment opportunities, pay, and access to resources. While there are benefits associated with using menstrual self-tracking apps, they can only be fully realized when these technologies are designed and operated ethically. I explore one possible path to improved corporate social responsibility by outlining the importance of developing theoretically informed corporate privacy policies that keep marginalized users' data private and secure. Drawing on insights from Nissenbaum's [9] theory of contextual integrity and feminist approaches to privacy that trouble the public/private dichotomy and the liberal democratic notion of a sovereign individual [10], [11], I explore what menstrual self-tracking apps could look like in the future, ultimately arguing that privacy is a feminist issue.
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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 | 0.059 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.066 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".