The Niakhar Social Networks and Health Project
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
This paper presents details of the design and implementation of the Niakhar Social Networks and Health Project (NSNHP), a large, mixed-methods project funded by the U.S. National Institute of General Medical Sciences (NIGMS). By redressing fundamental problems in conventional survey network data collection methods, the project is aimed at improving inferences concerning the association between social network structures and processes and health behaviors and outcomes. Fielded in collaboration with an ongoing demographic and health surveillance system in rural Senegal, the NSNHP includes qualitative data concerning the dimensions of social association and health ideologies and behaviors in the study zone, two panels of a new social network survey, and several supplementary and affiliated data sets. •Longitudinal social network survey linked to pre-existing surveillance data•Addresses fundamental methodological constraints in previous social network data•Enables social network analyses of health beliefs, behaviors, and outcomes.
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 | 0.005 | 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 | 0.000 | 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