They aren’t all named Karen: digital dating in a racist (or white man’s) world
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dating technologies have evolved from chatrooms to geo-social networking applications (GSNAs), often called dating/hook-up apps. Using location data, GSNAs connect users based on proximity for romantic or intimate purposes. However, concerns arise regarding GSNAs, risky practices (Albury & Byron, Citation2016), mental health impacts (Filice et al., Citation2019), and safety (Choi et al., Citation2018; Gillett, Citation2018). Scholars have noted that GSNAs can foster racism (Conner, Citation2023; Matharu et al., Citation2023), highlighting the need for digital equity research. Using a collection of 2227 images from a single man who frequently used the apps, this study examines women’s profiles to reveal a continuum of digital White supremacist ideologies (DWSI). We present visual interpretations on how conscious and unconscious “produced” pics can shape leisure spaces and address how to navigate different ethical considerations. Three key visual tropes emerged from the analysis: representations of right-wing American politics, political signalling as digital identity expression, and the memeification or mimicry of social movements. This study contributes to the field of digital leisure by interrogating how white supremacist ideologies are visually encoded and circulated within dating app spaces. We offer an arts-based and semiotic approach to interpret how digital publics are navigated through everyday leisure technologiess representing the data with art comics and narrative vignettes.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it