The formal demography of kinship V: Kin loss, bereavement, and causes of death
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The death of kin has psychological, physical, and economic effects on other members of a kinship network. Recently developed formal demographic models provide the deaths of kin, of any kind, at any age of a Focal individual. However, causes of death have yet to be accounted for. Objectives: Our objective is to extend the matrix kinship model to analyze losses of kin by cause of death, given age-specific schedules of risk due to each cause. Methods: The projection matrix is enlarged to include multiple absorbing states representing the age at death and the cause of death of kin at each age of Focal. The fertility matrix is enlarged to include production of living kin and set births by dead kin to zero. Results: The model provides deaths experienced at each age and accumulated up to each age of Focal, by cause of death and age at death. Causes of death are competing risks, permitting the study of how the elimination of one cause displaces bereavement across kin types and age groups of the bereaved. As an example, we analyze kin death experiences attributable to each of the leading 15 causes of death in the United States non-Hispanic white female population. Contribution: Studies of the death of kin and bereavement of survivors can now take into account diverse causes of death, each with its own age schedule of risks. These results provide novel understandings of how different causes of death influence kinship structures and bereavement experiences among surviving kin.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it