Transcending the “Black Raven”: An Autoethnographic and Intergenerational Exploration of Stalinist Oppression
Bibliographic record
Abstract
Many of Canada’s aging immigrants were displaced persons in Europe post-WWII and have internalized psychological effects of their traumatic past within a society that tends to marginalize or pathologize them. While early collective trauma literature focuses on individualized, psychotherapeutic approaches, more recent literature demonstrates the importance of externalizing and contextualizing trauma and fostering validating dialogue within families and community systems to facilitate transformation on many levels. My research is an autoethnographic exploration of lifespan and intergenerational effects of trauma perceived by Russian Mennonite women who fled Stalinist Russia to Germany during WWII and migrated to Winnipeg, Canada, and adult sons or daughters of this generation of women. Sixteen individual life narratives, including my own, generated a collective narrative for each generation. Most participants lost male family members during Stalin’s Great Terror, verschleppt, or disappeared in a vehicle dubbed the Black Raven. Survivors tended to privilege stories of resilience – marginalizing emotions and mental weakness. The signature story of many adult children involved their mother’s resilience, suppressed psychological issues, and emotional unavailability. Results underline the importance of narrative exchange that validates marginalized storylines and promotes individual, intergenerational, and cultural story reconstruction within safe social and/or professional environments, thus supporting healthy attachments.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".