Does income moderate basic relationship processes?
Bibliographic record
Abstract
Objective: This study explores whether household income moderates the predictive association from adaptive processes (positive and negative interactions and commitment), enduring vulnerabilities (psychological distress), and stressors (financial strain) to future relationship satisfaction? Background: Theory and research have long conceptualized socioeconomic status as a predictor of couple relations, but recent work questions whether socioeconomic status may moderate basic couple relationship processes. Method: This study used data from a U.S. national sample of 927 adults aged 18-34 years in a cohabiting (marital or nonmarital) different-sex partnership (66% female; 22% non-White; 47% earned a high school diploma or GED as their highest education credential) surveyed five times at 4-to 6-month intervals. A series of latent curve models with structured residuals were used to examine between- and within-person associations. Results: Robust between-persons associations emerged consistent with prior literature (e.g., those with more positive and less negative interactions, higher commitment, lower psychological distress, and less financial strain reported higher relationship satisfaction). One robust longitudinal association emerged at the within-person level: higher than typical negative interactions predicted intraindividual decreases in future relationship satisfaction. Within-person associations were more evident in the cross-section: at times when positive interactions and commitment were higher than one's own average and negative interactions and psychological distress were lower than average, relationship satisfaction was also higher than average. Income did not moderate any links with future relationship satisfaction. Conclusion: Results suggest that basic longitudinal processes in relationships operate consistently across income level.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".