Shades of <i>Égalité</i> : Educational Mobility and Ethnoracial Hierarchy Over Three Generations in France
Bibliographic record
Abstract
The long-term incorporation of immigrant-origin populations is a crucial question in liberal democracies. While much research has focused on the second generation, less is known about the grandchildren of immigrants. Investigating this “third generation” is key to assessing whether migration societies offer equal opportunity to their members regardless of their origins—that is, whether family background shapes life chances in a similar way among immigrant and native families. Here, we gauge the influence of ethnoracial origins on life chances in the long run by studying trajectories of intergenerational educational mobility among immigrant and native families over three generations. Our study is set in France, a major country of immigration in Europe, where a national narrative of immigrant integration and equality across ethnic origins has long prevailed. We show substantial catching up in educational attainment and higher social fluidity in immigrant families, for whom the grandparental educational starting point was very low. The grandchildren of Southern European immigrants converge with natives in their mobility patterns, suggesting equal opportunities. Despite a partial convergence, the grandchildren of North African immigrants experience a distinct mobility regime and enduring educational disadvantage. Altogether, our results suggest the existence of an ethnoracial hierarchy, whereby Southern European families experience educational destinies broadly comparable to those of natives, while ethnoracial origins durably shape the educational trajectories of North African families.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".