A Voice Behind the Headlines: The Public Relations of the Canadian Jewish Congress During the Holocaust
Bibliographic record
Abstract
The Canadian Jewish Congress (CJC), the main lobbying group for Canadian Jews during the Holocaust, advocated on behalf of both Canadian and European Jewry by employing a sophisticated public relations strategy. This article investigates three intertwined campaigns to publicize Canadian Jewish war efforts, to raise awareness of the extermination of European Jews, and to advocate that Canada accept refugees. It argues that the CJC used data-driven publicity to demonstrate Jewish loyalty to Canada, which subsequently allowed them to bring attention to Jewish extermination in the non-Jewish press and spurred sustained coverage of the topic. After Jewish extermination became clear, they worked behind the scenes with their allies and used the press to convince the Canadian government to rescue several hundred refugees. By showing the hidden efforts and unknown successes of Jewish organizations, we learn that, while still limited in power, their advocacy methods achieved more than is usually acknowledged. This article breaks with the methodological approach of asking only ‘who knew what and when?’ in press responses to the Holocaust. Instead, it asks how and why stories about the Holocaust made the news. In so doing, it de-emphasizes the decisions of journalists, editors, and publishers and demonstrates the Jewish voice behind stories in the non-Jewish press.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.055 | 0.029 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".