Femtech in context: A critical conceptual (re)view
Bibliographic record
Abstract
Emerging as a commercial category in 2016, 'femtech' has been publicly celebrated as a category of consumer-based digital health technologies designed to support the unmet and systemically marginalized health needs of women in areas such as menstruation, fertility, pregnancy, postpartum, and menopause, through data-driven apps, wearables, and self-diagnostic tools. Since its emergence, the term femtech has become culturally significant and has taken on a life of its own across commercial, public, and healthcare discourses. Despite the growth of femtech scholarship, clarity is lacking on how different disciplines have challenged the assumptions about sex, gender, health, technology, and innovation that shape dominant understandings of 'who' femtech is for (i.e. fem) and 'what' it constitutes (i.e. tech). Motivated by this research gap, a critical conceptual review was conducted to provide new entry points into critical debates. This article novelly adapts 'diffractive reading' as a methodological approach to bring disciplinary perspectives on femtech into conversation with one another across anthropology, computer science, cultural studies, gender studies, information studies, law, media studies, medicine, and science and technology studies. This article focuses on insights drawn between critiques of femtech which trouble the ideologies, discourses, and practices that shape dominant understandings of 'fem' and 'tech'. In thinking through and with the conceptual boundaries of femtech, this review underscores the ongoing need to examine femtech's role in shaping global dynamics of reproductive, labor, and environmental justice, in addition to neoliberal approaches to healthcare more broadly.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.017 | 0.131 |
| Scholarly communication | 0.030 | 0.045 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".