Understanding Within-Family Variability in Children's Responses to Environmental Stress
Bibliographic record
Abstract
INTRODUCTION Perhaps surprisingly, children can be exposed to very similar life experiences and yet they will be affected by these experiences in very different ways. In other words, similar environmental experiences do not result in children developing more similarly to one another. Several types of evidence suggest this. One type of evidence comes from twin studies in which it is possible to partition variance into genetic and environmental influence. Such studies show that once genetic effects have been controlled, siblings tend to be more dissimilar than similar on emotions and behavior (Plomin & Daniels, 1987). This is the case even though siblings are raised in the same home and exposed to, we assume, many of the same environmental influences. This suggests enormous variability in the ways in which individuals respond to environmental influences. Surprisingly, this is also the case at high levels of psychosocial adversity. We might think that being raised in a highly stressful environment would have an adverse effect on all children. It is clear, however, that this is not the case (Luthar, Cicchetti, & Becker, 2000). Even under highly adverse conditions such as living through wars in which loved ones are killed (Howard & Hodes, 2000) or being raised by parents with serious mental health problems (Jaffee et al., 2003; Niemi et al., 2004) there is still variability in children's responses to such stressors. For some, the exposure is associated with compromised development. For others, no evidence for behavioral or emotional compromise is evident.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".