Fraught claims at the intersection of biology and sociality: Managing controversy in the neuroscience of poverty and adversity
Bibliographic record
Abstract
In this article, I examine how a subfield of researchers studying the impact of poverty and adversity on the developing brain, cognitive abilities and mental health respond to criticism that their research is racist and eugenicist, and implies that affected children are broken on a biological level. My interviewees use a number of strategies to respond to these resurfacing criticisms. They maintain that the controversy rests upon a fundamental misunderstanding of their work. In addition, they use what I term 'plasticity talk', a form of anti-determinist discourse, to put forth what they believe is a hopeful conception of body and brain as fundamentally malleable. They draw attention to their explicit intentions to use scientific inquiry to mitigate inequality and further social justice - in fact, they believe their studies are powerful evidence that add to the literature on the social determinants of health. Though they may be interested in improving lives, they argue that their aims and means have little in common with programs trying to 'improve' the genetic stock of the population. I argue that theirs is a fraught research terrain, where any claims-making is potentially treacherous. Just as their studies of development refuse dualistic models, so too do their responses defy dichotomous categorization.
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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 | 0.045 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.185 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.012 | 0.019 |
| 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".