The year of the “virtual date”: Reimagining dating app affordances during the COVID-19 pandemic
Bibliographic record
Abstract
The coronavirus disease-19 pandemic introduced a crisis of safety and relevance for dating apps, as their affordances for facilitating in-person encounters posed the risk of viral transmission. This article examines how eight apps primarily catering to heterosexual markets responded to the pandemic through changes to socio-technical arrangements, new user prescriptions, and the curation of corporate data and success stories. By analyzing corporate social media and promotional materials alongside in-app developments, we find that these companies reimagined app affordances to promote “virtual dating,” a set of practices and symbolic meanings that prioritize visual, synchronous digital interaction as the most responsible, reliable, and successful dating approach to the pandemic. Virtual dating centers apps as databases of potential partners while prescribing modes of use aimed toward affective relief, displays of authenticity, and romantic courtship. This reimagining counters moral panics about digitally mediated relationships by resorting to heteronormative dating scripts while overlooking alternative app uses.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".