Analyser et comparer des tables de mobilité sociale à l’aide d’une approche relationnelle : continuité et lignes de fracture entre catégories socioprofessionnelles
Bibliographic record
Abstract
Ce travail a pour objectif de comparer des tables de mobilité sociale selon l’âge, le sexe, l’origine sociale, la date d’enquête (d’après les enquêtes FQP de l’INSEE de 2003 et de 2014/2015) en utilisant la nomenclature des professions en 31 catégories socioprofessionnelles et des outils de la Théorie des Graphes. Nous proposons de chercher s’il est possible de déduire une hiérarchie entre les catégories sur la base des flux significatifs en sur-représentation. Nous montrons qu’il existe une forte continuité entre les catégories et qu’une structuration de type centre(s)/périphérie(s) se dégage en particulier en 2014/2015 lorsque l’enquêté-e est comparé-e à son parent de même sexe que lui-elle.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".