Guides as Mediators of Memory: On the Holocaust and Antisemitism – 75 Years Later
Bibliographic record
Abstract
This article deals with the relationship between the Holocaust and antisemitism, focusing on the events of 2020-2021. The point of departure is the fifth World Holocaust Forum at Yad Vashem in Jerusalem, held under the slogan: “Remembering the Holocaust, fighting antisemitism”. The event took place at the invitation of Israel’s president, Reuven Rivlin, in advance of the 75th anniversary of the liberation of Auschwitz and International Holocaust Remembrance Day (January 23, 2020). Content analysis of the speeches given by presidents and prime ministers from around the world reinforce the insights of the Holocaust and the association with current-day antisemitism. In March 2020 the COVID-19 virus appeared, and a wave of antisemitism surfaced with it. Analysis of contents that appeared on websites and social networks reveals vitriolic antisemitism against Jews as generators of the virus, being the virus themselves.This study utilized the method of anthropologist Clifford Geertz (1926-2006), who established the interpretive approach to anthropology for analyzing culture contents. This, with regard to content analysis in general and to the contents of social networks and their contribution to antisemitism, in particular. Operation “Guardian of the Walls” in Gaza in 2021 further fanned antisemitism. Content analysis of websites and social networks portrays the Jewish soldier as a Nazi soldier and all Jews as murderers – with all the Holocaust symbols and Holocaust language.The study seeks to examine whether and to what degree the educational system in general and guides of youth trips to Poland as mediators of memory in particular, are prepared for the educational challenge of eradicating antisemitism in the post-Holocaust era. The research findings show that the challenge still awaits us. Education is an essential instrument in the battle against antisemitism but the educational system, both formal and informal, is not prepared.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".