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Record W2774436168 · doi:10.1111/1467-9566.12632

‘Love builds brains’: representations of attachment and children's brain development in parenting education material

2017· article· en· W2774436168 on OpenAlexaffabout
Glenda Wall

Bibliographic record

VenueSociology of Health & Illness · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsArgument (complex analysis)PsychologyDevelopmental psychologyAffect (linguistics)Attachment theoryParenting skillsChild developmentBrain developmentIdeal (ethics)Social psychologyPolitical science

Abstract

fetched live from OpenAlex

A focus on early brain development has come to dominate expert child rearing advice over the past two decades. Recent scholars have noted a reinvigoration of the concept of attachment in this advice and changes in the ways that attachment is framed and understood. The extent to which the concept of attachment is drawn on, the way it is framed, and the consequences for mothers, families and parent-child relationships is examined through a discursive analysis of a current Canadian parental education campaign. Findings support the argument that attachment is receiving a great deal of attention in brain-based parenting education programmes as children's emotional development becomes increasingly prioritized. Attachment is presented as needing to be actively and continually built through expert-guided empathetic and responsive parental behaviour, and is framed as crucial for the development of brain pathways that promote emotional strength and self-regulation in children. Attachment-building is also presented as requiring highly intensive parenting that falls overwhelmingly to mothers. The parent-child relationship that is envisioned is one that is instrumental, lacking in affect and conducive to the creation of ideal self-regulating neo-liberal citizens.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.154
Threshold uncertainty score0.485

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.379
Teacher spread0.352 · 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.

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

Citations31
Published2017
Admission routes2
Has abstractyes

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