Small effects of selective migration and selective survival in retrospective studies of fertility
Bibliographic record
Abstract
In this paper, we assess the accuracy of fertility estimates that are based on the retrospective information that can be derived from an existing cross-sectional population.Swedish population registers contain the information on childbearing of all people ever living in Sweden and thus allow us to avoid any problems of selectivity by virtue of survival or of out-migration when we estimate 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 were living in Sweden at the end of 1999 and (ii) rates that also include the information on people who had died or emigrated before the turn of the century.We find that the omission of information on emigrated and deceased individuals, as the situation would be in any demographic survey, most often have negligible effects on our fertility measures.However, firstbirth rates of immigrants gradually become more biased as we move back in time from 1999 so that they increasingly tend to over-estimate the actual fertility of that population.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".