Algorithmic heteronormativity: Powers and pleasures of dating and hook-up apps
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
We propose the concept of algorithmic heteronormativity to describe the ways in which dating apps’ digital architectures are informed by and perpetuate normative sexual ideologies. Situating our intervention within digital affordance theories and grounding our analysis in walkthroughs of several popular dating apps’ (e.g., Tinder, Bumble, and Hinge) interfaces, promotional materials, and ancillary media, we identify four normative sexual ideologies—gendered desire, hetero and homonormativity, mononormativity, and shame—that manifest in specific features, including gender choice, compatibility surveys, and private chat. This work builds on earlier digital culture theorizing by explicitly articulating the reciprocal and gradational linkages between existing moral codes, digital infrastructures, and individual behaviors, which in the contemporary context work jointly to narrow the horizon of intimate possibility.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it