Bibliographic record
Abstract
Research framework: Over the past decade, social science research has been profoundly renewed by the measurement of socioeconomic inequalities, which no longer takes into account only socio-professional status, labor income, or qualifications, but also wealth. Family matters for inheriting wealth, saving it, or accumulating it through returns on investments. The accumulation of wealth brings into play the three major dimensions of kinship: descent, siblingship, and alliance. Objectives: To establish a dialogue between the literature on socio-economic inequalities and the social sciences of the family. Methodology: This introductory article is based on a literature review of various social science disciplines in economics, sociology, demography, history, anthropology, political philosophy and law. Results: This issue of the journal Enfances Familles Générations is a plea for the social sciences to take into account both family ties and family assets, based on the concrete issues of inheritance, marriage, indebtedness and home ownership. Inheritance, its accumulation, preservation and transmission are, in fact, concerns within families of all social backgrounds. Conclusion: Family dynamics - in interaction with the multiple actors in the family field - play a part in structuring economic inequalities within the family (especially gender inequalities) and between families (especially class- and race-based inequalities). But exploring inequalities in asset and debt also provides a better understanding of family relationships, since wealth participates in the making of the family. Contribution: This introductory article affirms the importance of studying family wealth strategies across all social classes, and of multiplying the historical, national and cultural contexts of study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".