Knowing me-knowing you: Reported personality and trait discrepancies as predictors of marital idealization between long-wed spouses.
Bibliographic record
Abstract
In previous research, marital idealization has emerged as a significant predictor of adaptation to widowhood, the psychological well-being of spouses of persons with dementia, and the physical health of older married adults over time. Despite the adaptive value of marital idealization, conceptual confusion regarding this phenomenon persists. To this end, the present study examines the degree to which marital idealization is predicted by personality traits relative to partner perceptions of their spouse's personality, and discrepancies between self- vs. spousal reports for both husbands and wives. Multilevel models were computed on the basis of responses from 125 couples married an average of 34 years. Marital idealization by husbands was predicted by his personality (i.e., lower neuroticism, openness to experience, agreeableness, and higher conscientiousness). In contrast, marital idealization by wives was predicted by trait discrepancies (i.e., being seen, and seeing one's spouse, more positively than she or he sees him- or herself). Conscientiousness emerged as the trait for which between-sex differences were most pronounced, whereas both conscientiousness and agreeableness were the traits most broadly associated with marital idealization by both spouses (intracouple trait averages and discrepancies between spousal reports). These results are discussed in relation to gender socialization and between-sex differences.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".