The Target Model for Genealogical Networks
Bibliographic record
Abstract
Several large-scale projects including FamilySearch, Ancestry, BALSAC (University of Quebec), and others have gathered incredible amounts of genealogical data ranging from millions to billions of individuals. To study the structure of this data, we propose a model that generates a genealogical network based on real-world genealogical data using two key features: (i) geodesic distance between couples prior to union and (ii) the number of children per couple. The distribution of the distance to a couples' nearest common ancestor in an observed community captures the global scale at which biological cycles form in the underlying genealogical network. Similarly, the number of children per couple captures the local structure given by the degree distribution in the genealogical network. Constructing imitation data which approximates a real-world network's structure and growth rate is desirable for use in generalizable machine learning models. This model, which we refer to as the Target Model, provides a foundation for further work in predicting family network growth and structure.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".