How to Find Mr/Miss Right? The Mechanism of Search Among Online Daters in Shanghai
Bibliographic record
Abstract
Although online dating tools have become increasingly diverse over the decades, little is known about the search strategies of individuals or their choices of using certain dating platforms. Based on interviews with 29 heterosexual, highly-educated daters conducted in Shanghai, we examine their strategies for finding a partner online. Online daters can be categorized into three distinct dating types depending on their mating goals and mate preferences: dating, xiangqin (matchmaking), and mixed. We investigated the underlying gendered factors that drove them to specific dating types and guided their choices of online dating platforms. Despite the heterogeneity in dating types, online dating exhibited homophily effects, which may reinforce social inequality in China’s marriage market. While existing research often contrasted online dating with “traditional venues” and used online dating to symbolize modernity, we illustrate the subtlety between xiangqin and dating, thereby challenging the widely-used dichotomy of traditionality and modernity in conceptualizing family-related behaviors.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".