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Record W1979420125 · doi:10.1177/0165025409350953

A transactional analysis of maternal negativity and child externalizing behavior

2010· article· en· W1979420125 on OpenAlexaff
Zohreh Yaghoub Zadeh, Jennifer M. Jenkins, Debra Pepler

Bibliographic record

VenueInternational Journal of Behavioral Development · 2010
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsYork UniversityUniversity of TorontoBC Innovation Council
Fundersnot available
KeywordsPsychologyNegativity effectDevelopmental psychologyDyadExternalizationTransactional leadershipTransactional analysisStructural equation modelingLongitudinal studySocial psychologyStatistics

Abstract

fetched live from OpenAlex

A transactional model was used to examine the reciprocal relationship between maternal negativity and child externalizing behavior over three time points. Data were collected from 1,479 children and their mothers every two years, as part of the National Longitudinal Survey of Children and Youth (NLSCY). Children were 10—11 years old at Time 1, 12—13 at Time 2, and 14—15 at Time 3. Measures of maternal negativity were obtained from both mothers and children, while measures of child externalizing behavior were obtained from children only. Structural Equation Modeling revealed that both members of the dyad influenced one another’s behavior, with evidence of a recursive feedback loop over time. These influences were not equal (across persons) or stable (across time). Children’s influence on the development of maternal negativity increased over time.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.333
Teacher spread0.310 · 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

Citations50
Published2010
Admission routes1
Has abstractyes

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