Naming the Precious Child: New Evidence of Intentional Family Planning in Historical Populations
Bibliographic record
Abstract
Can the names parents gave their children give us insights into how parents in historical times planned their families? In this study, we explore whether the names given to the firstborn child can be used as indicators of family-size preferences and, if so, what this reveals about the emergence of intentional family planning over the course of the demographic transition. We analyze historical populations from 1850 to 1940 in the United States, where early fertility control and large sample sizes allow separate analyses of the White and Black populations. We also analyze Norway from 1800 to 1910, where there was a much later fertility transition. A split-sample method allows automated scoring of each name in terms of predicted family size. We find a strong relationship between naming and family size in the U.S. White population as early as 1850, for the Black population beginning in 1940, and for the Norwegian population in 1910. These results provide new evidence of the emergence of "conscious calculation" during the fertility transition. Our methods may also be applicable to modern high-fertility populations in the midst of fertility decline.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".