Kinship, Demography, and Inequality: Review and Key Areas for Future Development
Bibliographic record
Abstract
Kinship relations play a crucial role in structuring populations and shaping individual outcomes. Differences in kinship among individuals, cohorts, and subpopulations are one important aspect of these structures. Demography and related disciplines have proposed sophisticated approaches to study kinship in recent years. We argue that the development of a demography of kinship that centers on these processes will help advance the field of demography as a whole. Here, we review four key substantive areas of kinship research in demography: (1) kin supply and intergenerational transfers; (2) demographic change; (3) kin loss; and (4) social stratification. For each area, we identify important gaps in the literature and avenues for future research. We then review available methods and data sources to advance each of these areas, and conclude with an agenda to foster the study of the demography of kinship in general and kinship inequalities specifically.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".