Sex Differences in Early-Age Mortality: The Preconception Origins Hypothesis
Bibliographic record
Abstract
The preconception origins hypothesis holds that some of the preconception and prenatal environmental factors that have been shown to determine the offspring sex ratio also explain sex differences in early-age mortality (Pongou 2013). It extends and complements the biological hypothesis, which affirms that the mortality sex gap originates in biological and genetic differences between the sexes. As such, it offers a broad framework for understanding changes in male–female differences in early-age mortality across space and over time. I argue that this hypothesis is consistent with the concurrent increase in the proportion of female births and in the relative mortality of female to male infants in the United States since World War II. / L’hypothèse des origines préconceptionnelles affirme que certains facteurs auxquels les parents sont soumis pendant la période préconceptionnelle et prénatale et qui déterminent le sexe de l’enfant expliquent partiellement les différences de mortalité entre les garçons et les filles en bas âge (Pongou 2013). Cette hypothèse généralise et complémente l’hypothèse biologique selon laquelle l’écart de mortalité entre les garçons et les filles provient des différences biologiques et génétiques entre les sexes. Ainsi, cette hypothèse offre un cadre général qui permet de mieux comprendre les changements dans la différence de mortalité entre les sexes dans le temps et l’espace. Je montre que cette hypothèse est cohérente avec l’augmentation parallèle de la proportion des naissances féminines et de la mortalité infantile des filles relativement à cette des garçons aux États-Unis depuis la fin de la deuxième guerre mondiale.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".