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Record W3092200832 · doi:10.1080/14616734.2020.1832548

Convergent validity and stability of secure base script knowledge from young adulthood to midlife

2020· article· en· W3092200832 on OpenAlexaff
Theodore E. A. Waters, Christopher R. Facompré, Or Dagan, Jodi Martin, William F. Johnson, Ethan S. Young, Jessica Shankman, Yoojin Lee, Jeffry A. Simpson, Glenn I. Roisman

Bibliographic record

VenueAttachment & Human Development · 2020
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsYork University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Aging
KeywordsPsychologyNormativeDevelopmental psychologyConvergent validityCohortKnowledge baseSocial psychologyPsychometricsStatistics

Abstract

fetched live from OpenAlex

Attachment theory posits that early experiences with caregivers are made portable across development in the form of mental representations of attachment experiences. These representations, the secure base script included, are thought to be stable across time. Here, we present data from two studies. Study 1 (N = 141) examined the degree of empirical convergence between the two major measures of secure base script knowledge in Study 2, we examined stability of secure base script knowledge from late adolescence to midlife combining data from both a high- and normative-risk cohort (N = 113). Study 1 revealed evidence for convergent validity (r = .50) and Study 2 revealed moderate rank-order stability (r = .43), which was not moderated by cohort risk status. Results support the validity of secure base script knowledge assessments and prediction that attachment representations show moderate stability across early adulthood and into midlife.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.077
GPT teacher head0.371
Teacher spread0.295 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2020
Admission routes1
Has abstractyes

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