Comment mieux identifier les mères adolescentes dans le recensement français ? Améliorations de la méthode du « décompte des enfants au foyer »
Bibliographic record
Abstract
Pour mener des études de fécondité différentielle selon le profil sociodémographique, notamment au sein de population de petite taille, la méthode du « décompte des enfants au foyer » (DEF) est un outil précieux. Pourtant la qualité de cette méthode souffre de quelques limites dont celle de sous-estimer la fécondité aux jeunes âges, notamment durant l’adolescence. Nous proposons dans cette contribution des améliorations de la méthode pour pallier cette limite. Nos propositions permettent de réduire de 50 % à 34 % la sous-estimation du nombre d’enfants nés d’une mère âgée de 18 ans ou moins. L’Enquête famille et logements (EFL), enquête couplée au recensement de la France en 2011, confirme le bien-fondé de notre approche et la valide dans 93 % des cas.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".