WIRED FOR SOCIAL INTERACTION: WHAT AN INTERDISCIPLINARY APPROACH FROM NEUROBIOLOGY, EVOLUTIONARY BIOLOGY, AND SOCIAL EDUCATION WORK CAN TEACH US ABOUT PSYCHOLOGICAL TRAUMA
Bibliographic record
Abstract
The human brain is equipped with physiological, neural, and cognitive capacities that enable it to respond flexibly to ever-changing natural and social conditions. This capacity of the brain to reorganize itself, called neuroplasticity, is particularly marked in childhood. The high malleability of the brain at this stage provides the basis for wide-ranging learning, but leaves it vulnerable to negative environmental and social factors. Neuroplastic events that occur in response to abusive or neglecting environments can strongly interfere with the adaptive shaping of neural pathways between the prefrontal cortex and the limbic system, compromising judgment and self-control. Traumatic experiences during development can also have other effects on brain plasticity that are mediated by epigenetic mechanisms, and these effects can impair the developing oxytocin system, adversely affecting attachment and bonding. Mature individuals seek out niches that match the internal mental structures shaped during their early years, and will even alter the environment to make it match the internal structures. In the case of those who suffered childhood abuse, this can lead to maltreatment of the next generation. Addressing the societal challenge of child abuse and maltreatment requires broad interdisciplinary endeavors, uniting neuroscientists and social education workers to break the vicious circle.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".