Swipe Culture & Women’s Health: Exploring Current Themes in Dating App Research
Bibliographic record
Abstract
"Dating apps are revolutionizing how we approach intimacy and digital communication, which has implications for our mental and sexual health. As a researcher, writer, and activist working in the digital health and sexuality space, launching this Special Collection is a passion project that has the potential to help us radically rethink women's health and the role that swipe culture plays in our lives." - Dr Treena Orchard, Guest Editor of the Special Collection In this seminar, I review the key perspectives adopted in contemporary dating app research that explores how these platforms are impacting women's lives, especially their relationships. I then dive deeper into the literature about health, which focuses mainly on mental health to the exclusion of other issues that directly shape women's well-being and orientation to the world. This includes intersections between dating app use and women’s physical, mental, and sexual health across the lifespan, and the positive or therapeutic aspects of women’s dating app use. Next, I provide a detailed overview of our special collection, which seeks to showcase innovative research and interdisciplinary perspectives on dating apps and women's health. We are especially interested in papers that critically examine the complexities of swipe culture, including its risks and benefits, and how these experiences are shaped by intersecting social, cultural, and technological factors. We are especially interested in research that highlights the work of scholars and/or research spaces from the Global South.
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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".