The Language of Trauma: A Linguistic Analysis of Interviews with Holocaust Survivors
Bibliographic record
Abstract
We performed quantitative analysis on transcriptions of 784 interviews with Holocaust survivors. The interviews were collected by the University of Southern California Shoah Foundation, and the first 15 minutes of each interview had been transcribed using automatic speech recognition. The survivors were an aging population as the interviews were conducted around fifty years after the end of the Holocaust. We used statistical methods and algorithms to analyze the data including keyness analysis, topic modeling, and emotionality analysis. We used the Contemporary Corpus of American English (COCA) as a comparative corpus for these analyses. Overall, we found that survivors prioritized themes of the Holocaust and their families in the interviews. Specific words and themes reoccurred across the corpus demonstrating a collective and consistent memory of trauma. Our emotionality analyses revealed that survivors used slightly more positive language and fewer words relating to anger, disgust, and fear than the speakers in our comparative corpus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".