The Human Toll of Collective Trauma: The Ravages of War and Persecution
Bibliographic record
Abstract
This paper explores the concept of historical trauma (HT) as an extension of post-traumatic stress disorder (PTSD), expanding the definition to encompass the accumulated emotional and psychological trauma across generations and lifespans. Coined by Lakota social work professor Maria Yellow Horse Brave Heart, HT refers to the enduring impact of abuse and displacement on marginalized groups, such as enslaved African Blacks, Native Americans, Indigenous people in Canada, and others globally. HT becomes ingrained in cultural memory, impacting individuals with symptoms including depression, survivor guilt, anger, substance abuse, hypervigilance, and more.Examining the distinction between history and collective memory, the paper delves into the effects of persecution and oppression on specific groups, emphasizing chronic and severe stress. The focus extends to genocides worldwide, exploring maternal stress's impact on fetal development, drawing insights from events like the Ice Storm in Quebec, the Leningrad siege, and the Dutch famine. The paper concludes by questioning whether interventions can mitigate the maternal/paternal transmission of stress-induced pathologies, providing avenues for further research and potential solutions to alleviate the enduring effects of historical trauma.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".