Attachment security and narrative elaboration
Bibliographic record
Abstract
A key means of getting to know someone is through the sharing of personal experience narratives, an ability that shows considerable individual variation. Past research has documented a relationship between narration in conversations between children and their mothers and attachment security. However, children's narrative contributions are often embedded in an ongoing conversation which may be structured differently by mothers who also have assessed the extent to which their children use them as a secure base. In the present project, these two measurements were independent. Children's narration to an attentive, but non-scaffolding, stranger was investigated to see whether that, too, would correlate with security as assessed by mothers. Participants were 32 4-year-old children and their mothers. The security of children's attachment to their mother was assessed using the revised parent-reported 90-item Q-Sort and correlated with two measures of narration. One was simple length in words of the three longest narratives told to a friendly stranger, and the other was a composite formed from specific scored narrative variables. Both narrative measures were significantly correlated with attachment security, even after partialling out the effects of gender, age, and receptive vocabulary.These results suggest that securely-attached children have internalized the inclination to disclose themselves by means of relating narratives of some length and have begun to generalize this to adults outside their family.
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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 | 0.022 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".