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Record W6964803661 · doi:10.26193/82rcp6

The Child and Parent Emotion Study

2022· dataset· en· W6964803661 on OpenAlexaboutno aff

Bibliographic record

VenueAustralian Data Archive · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsSocioemotional selectivity theoryLongitudinal studyMental healthTemperamentLongitudinal dataCompetence (human resources)CohortEmotion work

Abstract

fetched live from OpenAlex

Introduction: Parents shape child emotional competence and mental health via their beliefs about children’s emotions, by modelling emotion regulation skills, emotion-related parenting, and the emotional climate of the family. Much of the research to date has been based on small samples with mothers of primary school-aged children. The Child and Parent Emotion Study (CAPES) aims to examine longitudinal associations between parent emotion socialisation, child emotion regulation and socioemotional adjustment at four time points. CAPES will investigate the moderating role of parent gender, child temperament and gender, and family background. Methods: CAPES is an age-stratified longitudinal cohort study. CAPES recruited 1,992 parents of children aged 0–9 years and 264 prospective parents (i.e, pregnant parents of their first child) in 2018–2019. Parents are residents of six English-speaking countries (i.e., Australia, New Zealand, US, Canada, UK, Ireland). Participants completed online self-report surveys that included several measures of parent outcomes, including parent emotion socialisation (e.g., parents’ beliefs about children’s emotions, parents’ stress), and age-sensitive measures of child outcomes (e.g., child emotion regulation, child internalising problems). Between 2018 and 2021, three timepoints of data have been collected, in intervals of approximately 12 months. Data collection for time 4 will be completed by late 2022. This dataset includes three timepoints of data, for participants who consented to share their data (N=2,069).

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 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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.097
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.000

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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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