From friction to flow: Dyadic affective flexibility during and after conflicts predicts trajectories of mother–adolescent relationships.
Bibliographic record
Abstract
flexibility). At Wave 1, 201 adolescents (11-12 years old, 46.3% girls) and mothers (87.5% Caucasian) completed two consecutive discussions about everyday conflicts and happy memories, respectively. Dynamic flexibility was derived from second-by-second affect coding via state space grids, and reactive flexibility was assessed as the latent change in dynamic flexibility across discussions. Annually for 5 years, including periods during the COVID-19 pandemic (i.e., Waves 3-5), mothers reported feelings of closeness with the adolescents, and both dyad members identified and rated the intensity of conflicts with each other. Results revealed that greater dynamic and reactive flexibility predicted greater and increasing closeness particularly from early to mid-adolescence. Greater dynamic and reactive flexibility were also associated with less intense and less diverse conflicts overall but not developmental changes in conflicts. These findings have implications beyond the immediate dyadic interactions around conflicts, suggesting that real-time flexibility within the mother-adolescent emotional system may serve as a resilience factor that buffers against the strains of relationship adjustment during adolescence at a longer timescale. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".