Bibliographic record
Abstract
Evaluating a long-term methodological norm - the use of interviewers who have no prior social relationship to respondents - we compare response patterns across levels of interviewer-respondent familiarity. We differentiate three distinct levels of interviewer-respondent familiarity, based on whether the interviewer is directly acquainted with the respondent or their family, acquainted with the research setting, or is a complete outsider. We also identify three mechanisms through which variability in interviewer-respondent familiarity can affect survey responses: the effort a respondent is willing to make; their level of trust in the interviewer; and interview-specific situational factors. Using data from a methodological experiment fielded in the Dominican Republic, we then gauge the effects of each of these on a range of behavioral and attitudinal questions. Empirical results suggest that respondents expend marginally more effort in answering questions posed by insider-interviewers, and that they also lie less to insider-interviewers. Differences in responses to "trust" questions also largely favor insider-interviewers. Overall, therefore, local interviewers, including those whom, in blatant violation of the stranger-interviewer norm, have a prior relationship with the respondent, collect superior data on some items. And on almost no item do they collect data that are measurably worse.
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.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".