Towards understanding and halting legacies of trauma
Bibliographic record
Abstract
Echoes of natural and anthropogenic traumas not only reverberate within the physiology, biology, and neurobiology of the generation directly exposed to them but also within the biology of future generations. With the intent of understanding this phenomenon, significant efforts have sought to establish multi-generational legacies of experiences like stress, chemical exposures, nutritional impoverishment, and chemosensory experiences. From these studies, we are gaining new appreciation for how legacies of trauma come to be bequeathed to future generations. This review first outlines principles that merit attention in the study of multi-generational legacies of trauma. Next, it discusses causes and consequences that allow for such legacies to perpetuate across generations. Finally, we discuss silver linings of such legacies and how legacies of flourishing can be engineered. In summary, this review synthesizes our current understanding of the concept, causes and consequences of legacies of trauma and looks to opportunities to halt them.
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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 | 0.002 | 0.024 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".