Is the new cognitive neuroscience of social inequality equal? Deconstructing the current neurocognitive research on children’s attention
Bibliographic record
Abstract
Abstract The relation between socioeconomic status (SES) and various outcomes, such as cognitive ability, behavior, social skills and health, has been studied for over half a century. The general consensus in interpreting the results as been that low SES is necessarily associated with cognitive and/or behavioral pathologies or deficits. Contrary to this deficit hypothesis new evidence suggests that the differences between low- and high-SES populations may be due to cognitive preferences associated with the social context where children develop. Much of this evidence has come from developmental neuroimaging studies on attention and executive control generally showing that despite differences between low- and high-SES children in neural correlates, there are no behavioral differences. Still, from within the new cognitive neuroscience of social inequality the observed differences are used to argue that low-SES children have neurocognitive impairments needing intervention/remediation. Other current research shows that low SES is associated with elevated levels of stress, and that elevated levels of stress or treatments with stress-related neuropeptides can alter certain aspects of attention. Thus, variations in attention across different SES backgrounds may be mediated by environmental stress. We review critically the connections among SES, stress and attention as well as a number of ethical, methodological and theoretical implications for health research. We argue that the deficit hypothesis is too limiting and that a comprehensive explanation of the association between SES and attention, and possibly cognition in general, requires a much broader explanatory framework grounded in both ecological and developmental theorizing that takes social context seriously.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".