Tongue Ties or Fragments Transformed: Making Sense of Similarities and Differences between the Five Largest English-Speaking Jewish Communities
Bibliographic record
Abstract
The subjects of Jewish identity and Jewish communal vitality, and how they may be conceptualized and measured, are the topics of lively debate among scholars of contemporary Jewry (DellaPergola 2015, 2020; Kosmin 2022; Pew Research Center 2021; Phillips 2022). Complicating matters, there appears to be a disconnect between the broadly accepted claim that comparative analysis yields richer understanding of Jewish communities (Cooperman 2016; Weinfeld 2020) and the reality that the preponderance of that research focuses on discrete communities. This paper examines the five largest English-speaking Jewish communities in the diaspora: the United States of America (US) (population 6,000,000), Canada (population 393,500), the United Kingdom (UK) (population 292,000), Australia (population 118,000), and South Africa (population 52,000) (DellaPergola 2022). A comparison of the five communities' levels of Jewish engagement, and the identification of factors shaping these differences, are the main objectives of this paper. The paper first outlines conceptual and methodological issues involved in the study of contemporary Jewry; hierarchical linear modeling is proposed as the suitable statistical approach for this analysis, and ethnocultural and religious capital are promoted as suitable measures for studying Jewish engagement. Secondly, a contextualizing historical and sociodemographic overview of the five communities is presented, highlighting attributes which the communities have in common, and those which differentiate them. Statistical methods are then utilized to develop measures of Jewish capital, and to identify explanatory factors shaping the differences between these five communities in these measures of Jewish capital. To further the research agenda of communal and transnational research, this paper concludes by identifying questions that are unique to the individual communities studied, with a brief exploration of subjects that Jewish communities often neglect to examine and are encouraged to consider. This paper demonstrates the merits of comparative analysis and highlights practical and conceptual implications for future Jewish communal research.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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 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".