Distance or location? How the geographic distribution of kin networks shapes support given to single mothers in urban Kenya
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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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".