A voice behind the headlines : the public relations of the Canadian Jewish Congress during World War II
Bibliographic record
Abstract
This thesis examines the public relations campaigns of the Canadian Jewish Congress (CJC) during World War II within the framework of agenda setting theory. As the voice of Canadian Jewry, the CJC implemented a sophisticated public relations strategy that brought attention to their causes in the non-Jewish press. From September 1939, the CJC capitalized on the patriotic atmosphere fostered by the war and the Canadian government. In a data-driven publicity campaign that would last the war, the CJC systematically both encouraged and tracked war efforts among Canadian Jews to fuel patriotic stories about Jews that improved their reputation. After it became clear in the summer of 1942 that the Nazis had begun exterminating Jews in Occupied Europe, the CJC started an awareness campaign in the non-Jewish press. Congress organized a mass rally in Montreal, Toronto, and Winnipeg that leveraged their patriotic reputation and brought both immediate and lasting coverage of Jewish extermination. By the spring of 1943, Congress believed they could persuade Canadians to rescue a number of refugees. To prevent an antisemitic backlash, they worked behind the scenes with their ally, the Canadian National Committee for Refugees (CNCR) and the activist professor, Watson Thomson, on a press campaign that convinced both the public and Canadian government that Canada needed to rescue refugees in the name of common humanity.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.061 | 0.015 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".