Assessing Respondent Driven Sampling for Network Studies in Ethnographic Contexts
Bibliographic record
Abstract
Respondent Driven Sampling (RDS) is generally considered a methodology for recruiting “hard-to-reach” populations for social science research. More recently, Wejnert has argued that RDS analysis can be used for general social network analysis as well (where he labels it, RDS-SN). In this article, we assess the value of Wejnert’s RDS-SN for use in more traditional ethnographic contexts. We employed RDS as part of a larger social network research project to recruit n = 330 community residents (over 17 years of age) in Nain, a predominantly (92%) aboriginal community in northern Labrador, Canada, for social network interviews about food sharing, housing, public health, and community traditions. The peer referral chains resulted in a sample that was then analyzed for its representativeness by two means—a comparison with the Statistics Canada 2006 Census of the same community, and with house-by-house demographic surveys carried out in the community as part of our research. The results show a close fit with available community statistics and our own survey. As such, we argue that the RDS sampling used in Nain was able to provide a useful and near-representative sample of the community. To demonstrate the usefulness of the results, the referral chains are also analyzed here for patterns in intragroup and intergroup relationships that were apparent only in the aggregate.
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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 | 0.426 | 0.600 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".