"Don't know" responding to answerable and unanswerable questions during misleading and hypnotic interviews.
Bibliographic record
Abstract
"Don't know" (DK) responses to interview questions are conceptually heterogeneous, and may represent uncertainty or clear statements about the contents of memory. A study examined the subjective intent of DK responses in relation to the objective status of information queried, in the context of memory distorting procedures. Participants viewed a video and responded to answerable and unanswerable questions phrased in misleading or nonmisleading formats, while hypnotized or not hypnotized. Subjective meanings of DK responses were queried, and a recognition measure assessed the contents of memory. Lower DK and accuracy rates were consistently associated with unanswerable and misleading questions. One-third of DK responses were statements that the information had no not presented. When these were recoded, accuracy estimates for answerable questions decreased and more so for hypnotized participants. These results demonstrate that DK responses convey different types of information, thus accuracy estimates in studies that permit DK responses may be misestimated. Robust risks associated with asking unanswerable questions and asking questions at all were observed. Implications for working with DK responses during interviews are discussed.
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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 | 0.017 | 0.164 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".