Ideal Standards, Acceptance, and Relationship Satisfaction: Latitudes of Differential Effects
Bibliographic record
Abstract
We examined whether the relations of consistency between ideal standards and perceptions of a current romantic partner with partner acceptance and relationship satisfaction level off, or decelerate, above a threshold. We tested our hypothesis using a 3-year longitudinal data set collected from heterosexual newlywed couples. We used two indicators of consistency: pattern correspondence (within-person correlation between ideal standards and perceived partner ratings) and mean-level match (difference between ideal standards score and perceived partner score). Our results revealed that pattern correspondence had no relation with partner acceptance, but a positive linear/exponential association with relationship satisfaction. Mean-level match had a significant positive association with actor's acceptance and relationship satisfaction up to the point where perceived partner score equaled ideal standards score. Partner effects did not show a consistent pattern. The results suggest that the consistency between ideal standards and perceived partner attributes has a non-linear association with acceptance and relationship satisfaction, although the results were more conclusive for mean-level match.
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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 | 0.005 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".