The ‘First Three Years’ Movement and the Infant Brain: A Review of Critiques
Bibliographic record
Abstract
Abstract This article reviews a particular aspect of the critique of the increasing focus on the brain and neuroscience; what has been termed by some, ‘neuromania’. It engages with the growing literature produced in response to the ‘first three years’ movement: an alliance of child welfare advocates and politicians that draws on the authority of neuroscience to argue that social problems such as inequality, poverty, educational underachievement, violence and mental illness are best addressed through ‘early intervention’ programmes to protect or enhance emotional and cognitive aspects of children's brain development. The movement began in the United States in the early 1990s and has become increasingly vocal and influential since then, achieving international legitimacy in the United States, Canada, New Zealand, Australia, the UK and elsewhere. The movement, and the brain‐based culture of expert‐led parent training that has grown with it, has been criticised for claiming scientific authority whilst taking a cavalier approach to scientific method and evidence; for being overly deterministic about the early years of life; for focusing attention on individual parental failings rather than societal or structural problems, for adding to the expanding anxieties of parents and strengthening the intensification of parenting and, ultimately, for redefining the parent–child relationship in biologised, instrumental and dehumanised terms.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".