Differential susceptibility to the environment: An evolutionary–neurodevelopmental theory
Bibliographic record
Abstract
Two extant evolutionary models, biological sensitivity to context theory (BSCT) and differential susceptibility theory (DST), converge on the hypothesis that some individuals are more susceptible than others to both negative (risk-promoting) and positive (development-enhancing) environmental conditions. These models contrast with the currently dominant perspective on personal vulnerability and environmental risk: diathesis stress/dual risk. We review challenges to this perspective based on emerging theory and data from the evolutionary, developmental, and health sciences. These challenges signify the need for a paradigm shift in conceptualizing Person x Environment interactions in development. In this context we advance an evolutionary--neurodevelopmental theory, based on DST and BSCT, of the role of neurobiological susceptibility to the environment in regulating environmental effects on adaptation, development, and health. We then outline current thinking about neurogenomic and endophenotypic mechanisms that may underpin neurobiological susceptibility, summarize extant empirical research on differential susceptibility, and evaluate the evolutionary bases and implications of BSCT and DST. Finally, we discuss applied issues including methodological and statistical considerations in conducting differential susceptibility research; issues of ecological, cultural, and racial--ethnic variation in neurobiological susceptibility; and implications of differential susceptibility for designing social programs. We conclude that the differential susceptibility paradigm has far-reaching implications for understanding whether and how much child and adult development responds, for better and for worse, to the gamut of species-typical environmental conditions.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".