Distance or location? How the geographic distribution of kin networks shapes support given to single mothers in urban Kenya
Why this work is in the frame
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Bibliographic record
Abstract
With increasing urbanisation and mobility underway across sub‐Saharan Africa, kin groups are becoming spatially dispersed. The extent of support provided by kin to one another is likely to vary with this geospatial positioning. Because most data collection is restricted to the co‐residential household, we have little knowledge of the geospatial dimensions of kin groups of which a large part is beyond household boundaries, and even less insight into how spatial variation might impact on intra‐familial support patterns. Drawing on recently collected data on single mothers and their kin in Nairobi, Kenya, we describe the geospatial positioning of non‐residential kin; examine the relationship between objective and subjective measures of distance and location of kin and support for single mothers; and analyse the relationship between kin clustering and receipt of support. Our results show several important findings. First, financial support from non‐residential kin is geographically quite dispersed but emotional support is more concentrated among kin living near the mother. Second, whereas there is no effect of the objective measures on financial or emotional support, we find strong effects of subjective measures. Third, we find that the clustering of kin around the mother by distance has no effect on either outcome but having the majority of kin living in rural areas has a negative effect on emotional support even after controlling for distance between kin and kin location.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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