Bibliographic record
Abstract
Tables 3.1 Jewish population of Canada, 1901-2021 31 3.2 Canadian Jewish, non-Jewish, and total populations by age cohort, 2011 32 3.3 CMAs with a Jewish population of more than 1,000, 2011 33 4.1 Hate crimes by top ethnic, racial, or religious group, Canada, 2019, and Toronto, 2020 47 5.1 Enrolment in non-Orthodox and Orthodox Jewish day schools, Toronto and New York City, 2018 59 5.2 Participation in Jewish education, per cent by city and country 60 5.3 "How important is each of the following in what being Jewish means to you?" Toronto, 2018, in per cent 63 6.1 Jewish population of Canada by degree of Jewishness, 2011 76 6.2 Ordinary least squares (OLS) regression predicting degree of Jewishness for Canadians, ages 25-64, 2011 (n = 26,660) 80 7.1 Jewish socialization and residential concentration by city, 2018 (n = 2,335) 93 7.2 Per cent of Jews by density of Jewish population in city FSAs, 2018 94 7.3 General linear model (GLM) regression predicting Jewish residential clustering in Canada (n = 1,740) 94 8.1 Income measures for Jews and non-Jews after taxes and transfers, Canada, 2010 102 8.2 Self-reported gross annual income, 2017, and socio-economic characteristics, 2018, of Jewish immigrants and non-immigrants in four cities 105 8.3 Ordinary least squares (OLS) regression predicting household income after taxes and transfers by city, 2010 109
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".