Lethal Stereotypes: Hair and Eye Color as Survival Characteristics During the Holocaust<sup>1</sup>
Bibliographic record
Abstract
In spite of many false negatives and false positives quite familiar to the people of Nazi‐dominated Europe, dark hair and eyes were salient among the physical stereotypes of Jews that the Nazis promulgated along with psychosocial ones. Many narratives of the Holocaust refer to someone surviving because he or she “did not look Jewish,” and others being caught and killed because they did. A quantitative test of the validity and impact of this attribution showed that a higher proportion of Holocaust survivors than of a North American Jewish control group had light‐colored hair, eyes, or both during the relevant period. The paper discusses possible reasons why these were survival characteristics under the conditions of the Holocaust, the possible short‐ and long‐term effects of such selectivity, and implications for stereotyping in other situations of ethnic persecution and genocide.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".