Understanding sensitivity: lessons learned from the legacy of Mary Ainsworth
Bibliographic record
Abstract
On the basis of extensive home observations, Ainsworth proposed that a mother's sensitivity to her infant's signals is the primary determinant of attachment security. Although subsequent research has found a relationship between sensitivity and attachment security, the effect sizes are much smaller than those reported by Ainsworth. In addition to the amount of observation time that might account for the effect size difference, we consider Ainsworth's focus on understanding the organizational structure of relationships. We coded 30 minute video records of interactions between 64 mother-infant dyads from semi-structured home observations conducted at 10 months of age. Coding consisted of writing a narrative summary of the interactions, annotating a completion of Ainsworth's rating scales of acceptance, accessibility, cooperation and sensitivity and then describing the mother's behavior using the Maternal Behaviour Q-set. Sensitivity scores derived from the Q-sort descriptions were robustly related (r = .65) to secure-insecure classifications in the Strange Situation conducted at 13 months. We reflect on the process of assessing Ainsworth's construct of sensitivity.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".