Small Effects of Selective Migration and Selective Survival in Retrospective Studies of Fertility
Bibliographic record
Abstract
In this study, we assess the accuracy of fertility estimates that stem from the retrospective information that can be derived from an existing cross-sectional population. Swedish population registers contain information on the childbearing of all people ever registered as living in Sweden, and thus allow us to avoid problems of selectivity by the virtue of survival or nonemigration when estimating the fertility measures for previous calendar periods. We calculate two types of fertility rates for each year in 1961-1999: (i) rates that are based on the population that was living in Sweden at the end of 1999, and (ii) rates that also include information on people who had died or emigrated before the turn of the twentieth century. We find that the omission of information on individuals who had emigrated or died, as the situation would be in any demographic survey, most often have negligible effects on fertility measures. However, first-birth rates of immigrants gradually become more biased as we move back in time from 1999 so that they increasingly tend to over-estimate the true fertility of that population.
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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 | 0.213 | 0.537 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".