Rethinking empathy: Emotions triggered by the Holocaust among the Muslim-minority in Germany
Bibliographic record
Abstract
In the last decade there has been widely shared discomfort about the way Muslim minority Germans engage with the Holocaust. They are accused of not showing empathy towards its Jewish victims and, as a result, of not being able to learn the necessary lessons from this massive crime. By focusing on instances in which the emotional reactions of Muslim minority Germans towards the Holocaust are judged as not empathetic enough and morally wrong, this article explores how Holocaust education and contemporary understandings of empathy, in teaching about the worst manifestation of racism in history, can also at times be a mechanism to exclude minorities from the German/European moral makeup and the fold of national belonging. Expanding from Edmund Husserl’s embodied approach to empathy to a socially situated approach, via the process of paarung, allows us to reinterpret expressions of fear and envy, currently seen as failed empathy, as instances of intersubjective connections at work. In my reinterpretation of Husserl’s ideas, the process of paarung that enables empathy to happen is not abstract, but pairs particular experiences happening at particular times and places under particular circumstances to individuals of certain social standing and cultural influences. An analogy can be made to shoes. Anyone has the capacity to imagine themselves in someone else’s shoes. Nevertheless, the emotional reactions the experience triggers in each person will be shaped by individual past experiences and social positioning. Hence, grandchildren of workers who arrived in Germany after World War II to rebuild the country resist an ethnicized Holocaust memory and engage with it keenly through their own subject positions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".