The Outsourcing of Online Dating: Investigating the Lived Experiences of Online Dating Assistants Working in the Contemporary Gig Economy
Bibliographic record
Abstract
A small cottage industry emerging within the larger gig economy is online dating assistant (ODA) companies that allow paying clients to outsource the labor associated with online dating, including profile development, date selection and matching, and even interaction (i.e., ODAs assume their clients’ identities to exchange messages with other [unsuspecting] daters to secure face-to-face dates). The newness of this industry presents an opportunity to investigate the lived experience of remote employees working in an up-and-coming virtual organization. Through interviews with six ODAs, we explored motivations, day-to-day workflow, and development of work identities. Analysis uncovered unique challenges ODAs faced when performing the “human-based” tasks of online dating, which differed starkly from other popular services being bought and sold in the gig economy (e.g., rideshare, food delivery). Findings also show how ODAs engage in pragmatic and critical sensemaking as they navigate the specific challenges associated with ODA labor, and those created by remote work and gig labor, more generally.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".