Moving beyond the household: Innovations in data collection on kinship
Bibliographic record
Abstract
Across settings, it has been shown that the co-residential household is an insufficient measure of family structure and support. However, it continues to be the primary means of population data collection. To address this problem, we developed a new instrument, the Kinship Support Tree (KST), to collect kinship structure and support data on co-residential and non-residential kin and tested it on a sample of 462 single mothers and their children in a slum community in Nairobi, Kenya. This instrument is unique in four important ways: (1) it is not limited to the co-residential household; (2) it distinguishes potential from functional kin; (3) it incorporates multiple geospatial measures; and (4) it collects data on kin relationships specifically for children. In this paper, we describe the KST instrument, assess the data collected in comparison to data from household rosters, and consider the challenges and feasibility of administration of the KST.
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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 | 0.030 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".