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Record W1982499851 · doi:10.1080/14616734.2014.900094

Understanding sensitivity: lessons learned from the legacy of Mary Ainsworth

2014· article· en· W1982499851 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAttachment & Human Development · 2014
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsWestern UniversityUniversité LavalUniversity of Guelph
Fundersnot available
KeywordsPsychologySensitivity (control systems)Cognitive psychologyDevelopmental psychologyCognitive science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.233
GPT teacher head0.399
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it