“Tell Them Not to Hate”: Words of Witness and Sacred Imperatives. Richard Kool (Ed.)
Bibliographic record
Abstract
Dr. Richard Kool: For those of us growing up in families profoundly touched by the Holocaust, there seemed to be two situations: either our parents rarely said anything about their experiences, or they often or always spoke of their experiences. In the former situation, we knew something was wrong: grandparents, uncles, and cousins were missing from our lives and we didn’t know why. They weren’t spoken of: we didn’t know what had happened, and knew we weren’t supposed to know. Or, we knew about those grandparents, aunts, cousins: we knew about them and we knew exactly what happened to them; we knew about their murders at the hands of the Nazis and other European anti-Semites. My family’s secrets were hidden until 1994, when, at the Victoria Yom Ha’shoah service, I realized I needed to understand what happened to my mother. Rabbi Reinstein’s influence at that time was an important part of my journey to uncovering her history as a Dutch teenager in hiding. Hearing Victor’s talk in Victoria in January 2020, I realized I still owed a large debt to him. This elaboration of his presentation, featuring images of the people he spoke about, is an offering of gratitude to him for all the gifts he’s given me and my entire family. About this project: Dr. Jordan Stanger-Ross,, Department of History, University of Victoria Rabbi Victor Reinstein visited Victoria in 2020 as a guest of the Defying Hatred Project at the University of Victoria. The project collaborated with Congregation Emanu-El to explore the local Jewish community’s responses to acts of hate and expressions of anti-Semitism and racism. Led by myself and fellow-historian Lynne Marks, political scientist Matt James, Germanic and Slavic Studies professor Helga Thorson, and Victoria Shoah Project member Frances Grunberg, the project was dedicated to critically examining the history and current possibilities of defying hatred in Victoria. When I met Rabbi Reinstein in Boston in the summer of 2019, I discovered (as many had before me) the warmth and depth of his reflections on these topics. This story, I felt, had to be told back home, in Victoria. Funds from the Social Science and Humanities Research Council of Canada made the visit possible.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".