NEUROSCIENTIFIC EVIDENCE AND CARE LEAVING: A MULTIDISCIPLINARY CRITICAL COMMENTARY
Bibliographic record
Abstract
While neuroscientific literature suggests that some parts of the brain are not fully developed until the mid-20s, public discourse is skewed toward early child development (ECD) because of its supposed long-term economic benefits. Some researchers have gone so far as to say that society overinvests in remedial programs for disadvantaged adolescents. Such claims resist advocacy efforts for extended care for children in out-of-home care and discourage policy and legislative concerns regarding investing in early adulthood. In this commentary, we unpack the literature on brain development and critically discuss its selective use by legislators and policymakers for investments in ECD. Despite the availability of neuroscientific and economic evidence, it is not prominent in the discourse surrounding supportive interventions like extending care. Using Bourdieu’s theory of social reproduction, we discuss how preference is given to only the type of knowledge that preserves the social structures that work to ensure the multigenerational flow of capital among dominant groups. Also, social institutions act within the dimensions set by the social structure, constantly shaping and reshaping ways of facilitating capital preservation among the upper classes. We conclude that, in addition to moral argument, the current neuroscientific evidence may support investment in extended care programs.
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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 | 0.030 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.062 | 0.069 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".