It takes two to talk: Longitudinal associations among infant–mother attachment, maternal attachment representations, and mother–child emotion dialogues
Bibliographic record
Abstract
Research on the attachment-dialogue link has largely focused on infant-mother attachment. This study investigated longitudinal associations between infant-mother attachment and maternal attachment representations and subsequent mother-child emotion dialogues (N = 50). Maternal attachment representations were assessed using the Adult Attachment Interview when children were 3 months, infant-mother attachment was assessed using the Strange Situation Procedure at 13 months, and mother-child emotion dialogues were assessed using the Autobiographical Emotional Events Dialogue at 3.5 years. Consistent with past research, the three organized categories of infant-mother attachment relationships were associated with later mother-child emotion dialogues. Disorganized attachment relationships were associated with a lack of consistent and coherent strategy during emotion dialogues. Autonomous mothers co-constructed coherent narratives with their children; Dismissing and Preoccupied mothers created stories that were less narratively organized. Although the Unresolved category was unrelated to classifications of types of mother-child discourse, mothers' quality of contribution to the dialogues was marginally lower compared to the quality of their children's contributions to the emotion discussion. Secure children showed highest levels of child cooperation and exploration. Autonomous mothers displayed highest levels of maternal sensitive guidance during emotion dialogues. We provide preliminary evidence for role reversal in dialogues between Preoccupied and Unresolved mothers and their children.
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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 | 0.002 | 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 | 0.001 | 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".