History’s Ethical Demand: Memory, Denial, and Responsibility in the Wake of the Holocaust
Bibliographic record
Abstract
What does it mean to discover an unspoken Nazi past in one’s own family? In a moment defined by chance and circumstance, I discovered that my German grandfather had joined the Nazi Party. Using my family’s struggle with memory as a site of inquiry, I examine the process of remembering, its transmission, and dissociation, particularly as it relates to past and present perpetrator groups. What lurks in the silences that are passed down between generations? How does our collective response to history’s atrocities shape what we what we know and remember as individuals? How do we define the moral obligations of memory, or understand the power of dissociation more than seven decades after the Holocaust? When does complacency in the face of past or present injustice make us complicit? Any answer to these questions points to the complexity of memory and the ethical demands of history. Connections between collective crimes of the past and social injustices in the present are considered and different forms of historical awareness and personal responsibility are discussed. In the face of overt prejudice and racism, “history’s call” and the work of psychoanalysis are inherently related.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".