Drawing leads to better recall than written or spoken methods in the cognitive interview for suspects
Bibliographic record
Abstract
Drawing the scene, recalling in reverse-order, and writing the testimony, may be useful in suspect interviews. However, the relative benefit of each instruction has not been assessed, and may be important for their inclusion in the Cognitive Interview for Suspects. The drawing and the written recall were expected to provide a benefit for information gathering; and all three instructions to provide a benefit for credibility analysis. Taking part in the cheating protocol (Russano et al., Citation2005), 242 participants played guilty or innocent mock-suspects. They were interviewed using a draw-and-tell vs. reverse-order vs. written recall vs. spoken recall instruction. The number of details (information gathering), and the number of Reality-Monitoring criteria (RM; credibility analysis) were measured. The draw-and-tell instruction helped to gather a significant higher number of details, compared to the three other recall methods. The written recall only had a benefit over the reverse-order recall. A higher proportion of RM criteria in deceptive statements was only found in the spoken condition. While the three mnemonics do not seem to improve credibility analysis, the draw-and-tell should be encouraged in suspects interviews. The benefit of reverse-order and written recalls may be reconsidered.
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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 | 0.013 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".