A longitudinal study of “we‐talk” as a predictor of marital satisfaction
Bibliographic record
Abstract
Abstract We‐talk, the use of first‐person plural pronouns over the use of singular pronouns when describing relationship events, is regarded as a linguistic indicator of marital relationship functioning. A meta‐analysis revealed that we‐talk is positively associated with relationship satisfaction. However, this literature is based mostly on cross‐sectional studies. This study tested the hypothesis that we‐talk would be associated with greater marital satisfaction over time. The sample comprised 77 couples enrolled in a longitudinal study of parents of preschool‐aged children. We‐talk was assessed at baseline during a marital discussion task about parenting challenges. Couples completed a measure of marital satisfaction at baseline, 6‐ and 12‐month follow‐ups. An actor–partner interdependence model examined the effects of spouses' we‐talk on their own marital satisfaction (actor effects), and their partners' marital satisfaction (partner effects). Results indicated a positive partner effect of we‐talk at baseline, but not over time. Moreover, there was an actor effect of we‐talk on changes in marital satisfaction over time, whereby low actor we‐talk was associated with a reduction in marital satisfaction, but high actor we‐talk was not associated with such a decrease. These findings suggest that greater cognitive interdependence, as indicated by we‐talk, may protect from declines in marital satisfaction over time.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".