Infant Mortality, Social Networks, and Subsequent Fertility
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
The research presented here addresses a longstanding but previously unsupported theoretical proposition related to social learning in the demographic literature. This is that individuals should respond to lower (higher) infant mortality of socially proximate others with decreased (increased) fertility. On a more general level this problem directly concerns the translation of the effects of macro-demographic forces such as mortality into micro-level individual behavior through social interaction. Using unique data that combine identification of individuals belonging to women's social networks with direct measurement of these network members' mortality experience, this research demonstrates such a linkage. Information concerning the level and variation in infant mortality available to women from a small Nepalese mountain population in their social networks is seen to influence the tempo of their fertility. It is suggested that the methodology employed has important implications for quantitative analyses of reciprocal processes of social construction and micro-macro linkages more generally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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