The Direct and Mediated Influence of Perceptual Variables on Physical Attractiveness and Relational Satisfaction in Romantically Involved Heterosexual Couples
Bibliographic record
Abstract
A 2015 PAID article, in a study of married couples, revealed that a rating of the physical attractiveness of one’s spouse was a significant predictor of relational satisfaction for both the husband and the wife. This unexpected finding proved to be the springboard for the present research. How is the perception of the attractiveness of one’s romantic partner related to factors other than objective beauty? A sample of 201 heterosexual couples (N = 402) explored which factors impact a rating of perception of physical attractiveness (PPA), including: objective physical attractiveness (OPA), standard demographics (e.g., age, ethnicity), power factors (e.g., wealth, position, accomplishments), physical characteristics (e.g., height, weight, BMI), personal qualities (e.g., self-esteem, emotional stability, agreeableness, social skills), physical health and vitality, and efforts to look good in a public or private setting. We then explored the impact of PPA on the relational satisfaction of the couple. The 2015 results were largely replicated, however, the present study revealed much more. Primary takeaways included: (a) There were substantial gender differences concerning the dynamic of factors that influenced the rating of PPA; (b) there were equally robust gender differences on the impact of the PPA (and other variables) on relational satisfaction; (c) OPA played a surprisingly minor role in the entire dynamic; and (d) structural equation models provided detail on the similarities and differences of dynamics for men and women. Avenues of future research are explored.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".