‘When I was your age’: Bearing Witness in Holocaust Education in Montreal
Bibliographic record
Abstract
If Holocaust survivor testimony has been the subject of enormous public attention, the educational activism of these survivors has been largely overlooked. Recorded interviews, like public testimonies, have tended to focus on their wartime experiences and specifically the violence they endured. Consequently, little time has been spent exploring their postwar lives and the central role that many have played in Holocaust education. Taking survivors’ work seriously allows us to view testimony from a different angle. The reasons they bear witness and how their stories touch and inform those who listen to them become just as significant as what is said. Les témoignages des survivants de l’Holocauste ont reçu une énorme attention publique, mais on a largement ignoré leur l’activismeéducationnel. Les entretiens enregistrés avec eux, comme leurs témoignages publics, ont eu tendance à porter sur leurs expériences des années de guerre et, plus précisément, sur la violence qu’ils ont subie. Par conséquent, on s’est peu soucié de leur vie après la guerre et du rôle central que plusieurs d’entre eux ont joué pour nous éduquer au sujet de l’Holocauste. Prendre le travail des survivants au sérieux, nous permet de voir les témoignages sous un autre angle. Les raisons pour lesquelles ces gens témoignent et les manières dont leurs récits touchent et informent ceux qui les écoutent deviennent tout aussi importantes que ce qu’ils disent.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".