Enclaves ethniques et stratégies résidentielles des Juifs à Toronto et Montréal
Bibliographic record
Abstract
This paper explores the residential strategies developed by different waves of Jewish migrants in Toronto and Montreal since their early establishment in Canada. Tracking the creation of synagogues and centres of worship, as well as Jewish schools, allows us to evaluate their impact on the urban landscape. Where and how were these enclaves built? What were the strategies that have prevailed with each new wave of immigrants to incorporate their culture within these particular landscapes? Whereas religious and ethnic affiliations were essential expressions of identity in those enclaves, French language became the dominant factor of integration for Moroccan Jews in Quebec during the 1960s and 1970s. The paradox of their establishment in the 1960s is that even though most of them spoke French and founded their schools and main institutions in that language, they chose to live within established Jewish enclaves, which were multi-ethnic and anglophone. Did religion trump language?Cet article explore les stratégies résidentielles développées par différentes vagues de migrants juifs à Toronto et à Montréal depuis leur établissement initial au Canada. Suivre la création de synagogues et de centres de culte, ainsi que les écoles juives, nous permet d’évaluer leur impact sur le paysage urbain. Où et comment ces enclaves ont-elles été construites ? Quelles ont été les stratégies qui ont prévalu à chaque nouvelle vague d’immigrants pour intégrer leur culture dans ces paysages particuliers ? Alors que les affiliations religieuses et ethniques étaient des expressions essentielles de l’identité dans ces enclaves, la langue française est devenue le premier facteur de l’intégration des Juifs marocains au Québec dans les années 1960 et 1970. Le paradoxe de leur établissement dans les années 1960 est que, même si la plupart d’entre eux parlaient français et ont fondé leurs écoles et leurs principales institutions dans cette langue, ils ont choisi de vivre au sein des enclaves juives, qui étaient multiethniques et anglophones. La religion a-t-elle pris le pas sur la langue ?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".