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 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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".