Initial Evidence that Individuals Form New Relationships with Partners that More Closely Match their Ideal Preferences
Bibliographic record
Abstract
An important assumption in interpersonal attraction research asking participants about their ideal partner preferences is that these preferences play a role in actual mate choice and relationship formation. Existing research investigating the possible predictive validity of ideal partner preference, however, is limited by the fact that none of it has focused on the actual process of relationship formation. The current research recruited participants when single, assessed ideal partner preferences across 38 traits and attributes, tracked participants’ relationship status over 5 months, and successfully recruited the new partners of 38 original participants to assess their self-evaluations across the same 38 traits and attributes. Using multilevel modeling to assess the correspondence between ideal partner preferences and self-evaluations within couple, the results showed a positive within-couple association that was not accounted for by personality similarity or stereotype accuracy. We discuss these results with respect to the current literature on the predictive validity of ideal partner preferences in relationship formation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".