Amish fertility in the United States: Comparative evidence from the American Community Survey and Amish population registries
Bibliographic record
Abstract
BACKGROUND: Quantitative studies of Amish population dynamics have been methodologically constrained by difficulties identifying Amish in national surveys. If Amish could be reliably identified in, for example, the American Community Survey (ACS), researchers could leverage its rich variables to document both demographic outcomes and their social predictors. OBJECTIVE: Cross-validate two methods for studying Amish populations by comparing fertility measures in the ACS with the Cross-sectional Amish Population and Environment Database-2010s (CAPED-2010s), a large administrative record database of North American Amish. METHODS: We identify potential Amish ACS respondents through combinations of the attributes (1) Pennsylvania Dutch language use, (2) absence of household telephone, and (3) farming. We then calculate fertility measures derived from both the CAPED data and ACS data samples (2000-2021). This comparative method allows us to assess whether the two samples produce demographic comparable estimates. RESULTS: Both methods produce remarkably consistent fertility statistics, including total fertility rates (just over six children), age-specific fertility rates (highest ages 20-29), and non-marital fertility (very low). CONCLUSIONS: The strong agreement between ACS- and CAPED-2010s-derived demographic estimates validates both approaches for studying Amish populations. CONTRIBUTION: The ACS's rich social variables complement CAPED-2010s' comprehensive demographic coverage, demonstrating the credibility of two separate large databases for studies of the Amish.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".