Using Semi-Projective Doll Play Methods to Classify Middle Childhood Children Into Four Attachment Types: Types Associations With Distinctive Psychosocial Adaptation
Bibliographic record
Abstract
This research presents an adapted version of the Attachment Doll Story Completion Task for children in middle childhood (ADSCT for m-c), a measure for classifying children’s representations of mother-child attachment relationships into four attachment types: secure, avoidant, ambivalent, and disorganized. The ADSCT enables interviewers to partially circumvent the sophistication and defensiveness of middle childhood children's story completions. A sample of 185 children in the 4th and 5th grades, and 50 mothers of children from one 4th and one 5th grade class of that sample participated in the study. Children underwent the ADSCT for m-c procedure. Homeroom teachers, classmates, and the child reported on the children’s psychosocial adaptation. A sub-sample of the mothers completed measures of maternal caring attitudes and practices. Associations between the different attachment types and distinct forms of adaptation showed that secure attachment exhibited positive social relationships and a low level of psychosocial and behavior problems; disorganized attachment showed the poorest adaptation, manifested in psychosocial problems, behavior problems, social problem, aggressiveness, and victimization. Avoidant attachment exhibited social problems, peer rejection, behavior problems, and compulsive thought. And ambivalent attachment showed social vulnerability, and intermediate level of adaptation, between the better functioning of the securely attached and the problematic functioning of the insecurely attached. Concurrent validity of the ADSCT for m-c with maternal attitudes and practices, and discriminate validity with reference to key cognitive variables were good.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".