Choix occupationnels et espérance de vie : une analyse par l'approche des données massives
Bibliographic record
Abstract
Dans ce mémoire, nous tentons d’évaluer si le choix de profession a un effet sur l’espérance de vie à l’aide de techniques d’apprentissage automatique. Nous utilisons des arbres d’inférence conditionnelle (CTree) afin d’obtenir des courbes de survie Kaplan-Meier qui permettent de prédire des taux de mortalité en fonction des déterminants sociodémographiques qui ont le plus grand pouvoir explicatif. À l’aide des données ontariennes et québécoises du recensement de 1991 couplées aux enregistrements de la base canadienne de données sur la mortalité de 1991 à 2006 nous avons pu déterminer qu’il existe une corrélation entre le choix occupationnel et l’espérance de vie d’un individu pour certains groupes. Tel qu’attendu, l’algorithme a identifié le sexe de l’individu comme principal prédicteur de l’espérance de vie. Nous obtenons que l’éducation et le statut de natif sont, respectivement, les variables plus influentes chez les hommes et chez les femmes des deux provinces.
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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 | 0.024 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".