The Predictive Validity of Ideal Partner Preferences in Relationship Formation: What We Know, What We Don't Know, and Why It Matters
Bibliographic record
Abstract
Abstract A great deal of research on interpersonal attraction implicitly assumes that stated ideal partner preferences guide the mate selection, and therefore relationship formation, process. Nevertheless, recent research has yielded contradictory results. Whereas some research has failed to demonstrate that ideal partner preferences influence attraction to actual potential romantic partners, other studies have provided empirical evidence for the predictive validity of ideal partner preferences following interactions with potential romantic partners. A new meta‐analysis on the predictive validity of ideal partner preferences concluded that people may not preferentially pursue potential partners that more closely match their stated preferences. This conclusion has significant implications for several empirical literatures that have relied on self‐reported ideal partner preferences to test hypotheses. We demonstrate, however, that the majority of the research on the predictive validity of ideal partner preferences, and thus research included in this meta‐analysis, focuses on interpersonal attraction or later relationship processes and not on individuals transitioning into actual new relationships. We suggest that research that directly focuses on the transition into actual relationships is needed before firm conclusions can be made regarding the predictive validity of ideal partner preferences in the formation of new relationships.
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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 | 0.107 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".