Encouraging and clarifying “don't know” responses enhances interview quality.
Bibliographic record
Abstract
Investigative interviewers seek to obtain complete and accurate accounts of events from witnesses. Two studies examined the influence of instructions about the use of don't know (DK) responses and of clarifying the meanings of DK responses on the quality of responding to questioning. Participants watched a video, and after a delay (Study 1, 30 min; Study 2, 1 week) were randomized to a DK encouraged, DK discouraged, or control group. They then responded to answerable and unanswerable questions, after which they clarified the meanings of DK responses. Across studies, individuals encouraged to use DK responses answered fewer questions and made fewer errors at initial questioning. Discouraged and control participants showed similar performance, suggesting that interviewees assume that DK responses are not desired unless otherwise instructed. Clarifying the meanings of DK responses revealed that a majority of DK responses were correct statements about the presence or nonpresence of information in the video. The encouraged group showed greater gains in output after clarification while maintaining lower errors. Encouragement and clarification of DK responses were each associated with higher diagnosticity that substantive answers were in fact correct responses to answerable questions. Encouraging DK responses and clarifying the meaning of DK responses leads to more accurate reports in response to questioning. Encouraging DK responses reduces the tendency to overreport, which can reduce the quality of responding. DK responses frequently convey different meanings that, if clarified, can lead to useful information about the occurrence or nonoccurrence of information.
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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 | 0.106 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".