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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".