Emotion Socialization as a Framework for Understanding the Development of Disorganized Attachment
Bibliographic record
Abstract
Abstract Recent years have seen the emergence of accounts of the origins of the Disorganized attachment relationship in early mother–infant interaction, each building on the pioneering work of Main and Hesse—dysfunctional emotional processes figure prominently in all these accounts. This paper applies a framework based on two complementary theories of emotion socialization, Gianino and Tronick's (1992 ) Mutual Regulation Model and Gergely and Watson's (1996 ) Social Biofeedback Theory, to suggest an emotion‐based mechanism consistent with recently proposed models of the development of Disorganized attachment. The framework is used to generate hypothetical accounts of the role of dysfunctional emotional processes and maladaptive emotion socialization in early mother–infant interaction in the development of Disorganized attachment along two distinct pathways, one associated with actual abuse of the infant and the other associated with maternal unresolved trauma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".