(Eventual) stability and change across partnerships.
Bibliographic record
Abstract
Does a new partnership differ from its preceding one? This study investigates whether relationship dynamics change as people transition from 1 partnership to another and examines a number of predictors that might explain variation in change trajectories. We draw on data gathered from 554 focal participants in the German Family Panel (pairfam) study surveyed at 4 time points spanning 2 intimate unions to answer these questions. Latent change score modeling results showed eventual stability in 5 of 7 constructs under investigation. When looking at overall change from Time 1 in Partnership 1 to Time 2 of Partnership 2, there were no mean-level changes in relationship and sexual satisfaction, perceptions of relational instability, or frequency of conflictual and intimate exchanges. Sexual frequency and partner admiration improved across partnerships. Further analyses showed much change unfolded in the interim; all constructs showed significant deterioration as Partnership 1 drew to a close, marked improvements as individuals moved from the end of Partnership 1 into their next union, and worsening across the 1st year of Partnership 2. Neuroticism and relationship length were the most consistent predictors of change across partnerships: Those in shorter Partnership 1 relationships and with higher neuroticism typically experienced decreases in functioning across partnerships. These findings provide support for an eventual stability conceptualization of relationship development across partnerships. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".