Recruiting an Internet Panel Using Respondent-Driven Sampling
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
Respondent-driven sampling (RDS) is a network sampling technique typically employed for hard-to-reach populations when traditional sampling approaches are not feasible (e.g., homeless) or do not work well (e.g., people with HIV). In RDS, seed respondents recruit additional respondents from their network of friends. The recruiting process repeats iteratively, thereby forming long referral chains. RDS is typically implemented face to face in individual cities. In contrast, we conducted Internet-based RDS in the American Life Panel (ALP), a web survey panel, targeting the general US population. We found that when friends are selected at random, as RDS methodology requires, recruiting chains die out. When self-selecting friends, self-selected friends tend to be older than randomly selected friends but share the same demographic characteristics otherwise. Using randomized experiments, we also found that respondents list more friends when the respondent’s number of friends is preloaded from an earlier question. The results suggest that with careful selection of parameters, RDS can be used to select population-wide Internet panels and we discuss a number of elements that are critical for success.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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