Reflections on addressing antisemitism in a Canadian faculty of medicine
Bibliographic record
Abstract
On my mother's side I am a child and grandchild of Polish Jewish Holocaust survivors who, from a UN Refugee Camp in post-war Austria, were resettled in Canada as refugees.On my father's side I am part of a large family of Orthodox Jewish religious scholars who moved from Lithuania to what is now Israel in the 1920s and 1930s.I was born in Jerusalem and raised in Montreal.In addition to my medical training and a master's degree in HPE, I have a doctorate from Oxford in Yiddish literature with a focus on publications by Holocaust survivors in post-war European refugee camps like the one in which my mother grew up.My husband and I, as well as our three Jewish school-aged children, are white-passing in appearance; I increasingly use that privilege to hide our Jewish identities under professional and personal circumstances when we are outside the Jewish community, including on or near the U of T campus.Professionally, I am an Internist and Education Scientist with clinical and faculty appointments in TFOM (including at the Wilson Centre and in the Department of Medicine), at Sunnybrook Health Sciences Centre, at U of T's Institute of Health Policy Management and Evaluation, at the Ontario Institute for Studies in Education, and at U of T's Anne Tanenbaum Centre for Jewish Studies.In those roles, I have been doing research and education related to social justice in HPE for over a decade.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.052 | 0.021 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.028 | 0.056 |
| Insufficient payload (model declined to judge) | 0.011 | 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".