Ultra-Orthodox fertility and marriage in the United States: Evidence from the American Community Survey
Bibliographic record
Abstract
BACKGROUND Amid low fertility rates in the industrialized world, some subpopulations have maintained high fertility rates.However, it has often been difficult to study these populations due to limitations in extant data sources. OBJECTIVEThis paper will demonstrate a method of measuring key demographic indicators for Ultra-Orthodox Jews using demographic and language variables in the American Community Survey (ACS). METHODSComparison of estimates of total fertility rates derived from ACS estimates of Yiddish and Hebrew speakers to related indicators from small surveys of American Jewish populations and data on same-sect fertility in Israel and the United Kingdom validates the use of Yiddish to identify Ultra-Orthodox Jewish respondents in the ACS. RESULTSACS-derived demographic estimates for Yiddish speakers closely approximate estimates derived for Ultra-Orthodox Jewish communities using other methods.Ultra-Orthodox Jews in America have high fertility but very low rates of teen fertility and marriage, and fairly egalitarian marriage ages.Ultra-Orthodox Jewish fertility is high but not necessarily uncontrolled. CONCLUSIONSACS language data can be used to study relatively small subpopulations with unique demographic characteristics. CONTRIBUTIONResearchers can use ACS language data to study other demographically unique subpopulations or to study Ultra-Orthodox Jews in more detail than was previously possible.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".