“Rescue Our Family From a Living Death:” Refugee Professors and the Canadian Society for the Protection of Science and Learning at the University of Toronto, 1935-19461
Bibliographic record
Abstract
Throughout the 1930s into the early 1940s, the University of Toronto was inundated with desperate letters for succor from European professors who were persecuted under Nazism. Many of the stories in these appeals outlined life and death situations. The university responded by hiring some of these professors, but vigorous debate erupted with the establishment of the Canadian Society for the Protection of Science of Learning in 1939. The Toronto Society, the most influential of the other, more smaller Societies in Canada, was struck as an organization to place refugee professors in Canadian universities. It is an excellent case study in analyzing the socio-economic, political, and intellectual responses to a humanitarian disaster. The Society brought to the fore the spectre of racism and anti-Semitism in various academic and social communities in Canada, and further supported the historical argument that the Immigration Branch in Ottawa had particular, oppositional agendas in dealing with refugees of particular ethnicities and cultures. The Society highlighted the tensions of altruism and practicality, accommodation versus discrimination, and intellectualism overwhelmed in a oft-times hostile anti-intellectual and defensive society. The rapid failure of the Society demonstrated that strategies used by Canadian professors to offer safe harbour for their fleeing European counterparts were far too powerless in the fight against entrenched beliefs and conformist understandings in higher education and society as a whole.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.078 | 0.021 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| 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".