The constellations of child fostering in Kenya: Considering location and distance
Bibliographic record
Abstract
BACKGROUNDWhile studies provide context on why mothers foster-out children, there is little discussion about where children reside, transitions in children's living arrangements over time, distance between fostered children and their mothers, and how such distance might influence mothers' relationships with children. OBJECTIVESWe aimed to: (1) examine the geographical location of fostered children and distance from mothers, (2) establish who fosters children and the mothers' relationships with caregivers, (3) determine transitions in children's fostering arrangements, including mobility within kin networks, and (4) explore mothers' perceptions of distance, location, and barriers to contact with fostered children. METHODSWe used innovative kinship-network data and in-depth interviews with mothers who have fostered-out children in Kenya.We mapped locations of fostered-out children using geocoded data, determining 'hot spots' while exploring distance from mothers, and analyzed qualitative and quantitative data to examine mothers' perceptions of distance as a barrier to maternal-child relationships. RESULTSFostered children live primarily in rural Kenya, and there is substantial fluidity in children's living arrangements.Mothers' relationships and contact with children are impacted by location and distance. CONTRIBUTIONOur study highlights kinship linkages and child fostering over time and space.It suggests vital areas in future research on fostering and kinship more broadly and demonstrates the
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".