‘Love builds brains’: representations of attachment and children's brain development in parenting education material
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".