Initial impressions of compatibility and mate value predict later dating and romantic interest
Bibliographic record
Abstract
Romantic first impressions seem to linger, but why? Few studies have investigated how romantic desire during initial interactions predicts later relational outcomes (e.g., later romantic interest, contact attempts) using a design that can tease apart different possible mechanisms (e.g., mate value, selectivity, compatibility). Across three speed-dating studies ( n = 559) with longitudinal follow-ups (including college and community samples, and a sample of men who date men), we investigated whether different components of initial romantic impressions predicted later romantic outcomes and relationship initiation. Using the social relations model, we partitioned initial desire at speed dating (determined from 6,600+ total dates) into partner effects (a date’s consensual desirability, e.g., mate value), actor effects (a participant’s general desirousness, e.g., selectivity), and relationship effects (a participant’s unique liking for a date over and beyond partner and actor effects, e.g., compatibility) to predict later evaluations (romantic interest, physical attraction, and desire to know better) and behaviors (direct messaging and going on dates). Meta-analyses across the three studies showed that, across 6,100+ follow-up reports, partner and relationship effects were especially strong predictors of relationship initiation variables. Consistent with evolutionary models of human pair bonding, these findings suggest that both consensually desirable traits and unique impressions of compatibility have lingering effects on relationship development, even from the moment that two potential partners meet.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".