Minimization, The Trojan Horse of Interviewing? Measuring Perceptions of Witness Interviewing Strategies
Bibliographic record
Abstract
Layperson perceptions of explicit and implicit witness interviewing tactics were examined. Canadian residents ( N = 293) read an interview transcript that contained a tactic (i.e., explicit threat or promise, one of four types of minimization, or no tactic) that aimed to persuade the witness to change his account. Participants were then asked to rate the amount of trouble the witness would be in if he (a) changed his account and (b) retained his original account, as well as their perceptions of the witness, interviewer, and tactic. Results showed that participants who viewed a tactic believed the witness would be in less trouble if he changed his account than if he retained his original account. All leniency-related strategies (i.e., explicit leniency and all minimization tactics) were rated as somewhat acceptable and respectful, frequently used, and legal for police to employ. Implications of these findings for witness interviewing 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 | 0.021 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".