Dreaming the memories of our parents: Understanding neurobiology of transgenerational trauma and the capacities for its healing
Bibliographic record
Abstract
Selma Freiberg once said that “trauma demands repetition”. What if actual trauma did not happen in real life of one particular person, but he/she feels that it was real, as it is repeated every night – in every dream? Do children and grandchildren of survivors of holocaust and of the pogroms dream the memories of their parents and grandparents? Does their imagination “make them up” or do they have a transgenerational connection to the traumatic past of their parents and grandparents, even if they were protected from knowing and hearing the horrors of what actually happened to their loved ones sometime one or two generations apart? Are these people born with some specific biological markers (e.g., lower cortisol levels)? Can fear be passed along from parents to children by smell? All these questions can be answered positively (see work of Jacek Debiec, Dias and Ressler, and many others), and can be explained on the level of neurobiology and epigenetics (thanks to contributions of Moshe Szyf and Michael Meaney from McGill University, and others). This presentation will offer some neuro-psychoeducational reflections on the topic of transgenerational trauma, its epigenetic transmission and its neuro-psycho-biological constructs, as well as a very personal touch, a personal story of growing up in a very nurturing and cultured, but very small family, and not knowing of the circumstances of “why small?” Disclosure of interest The author has not supplied his declaration of competing interest.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".