Bibliographic record
Abstract
5.1Grade 9 enrolment at TanenbaumCHAT, by annual tuition in 2010 constant dollars, 2010-2020 66 6.1 Degree of Jewishness by socio-economic characteristics 77 6.2 A causal model of degree of identity 82 8.1 Distribution of household income for Jews and non-Jews after taxes and transfers, by decile, Canada, 2010 103 10.1 Connectedness to Jewish life in city by desire for more connectedness: three main Jewish sub-communities, 2018 138 12.1 Mean residential dissimilarity index by religion, 2001 and 2011 172 12.2 Residential dissimilarity index for Muslims vis-à-vis other religious groups, 2001 and 2011 173 13.1 Negative attitudes towards Jews by residence, in per cent 188 13.2 Importance of religion/identity, Canada, Jews 2018 and Christians 2019, in per cent 192 16.1 Ethnic over-representation among UTMS graduates, Jews vs. selected non-Jewish Asian and Middle Eastern groups, 1918-2018 234 17.1 Identity maps of Jewish identity markers, Canada and seven other countries, 2013-2018 262 20.1 Probability of believing that Jews in Canada often face discrimination, by sample category 312 20.2 Probability of believing that antisemitism in the UK is a very big problem, by sample category 312 20.3 Probability of believing that antisemitism in France is a very big problem, by sample category 313
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) | 0.002 | 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".