Drawing conclusions: Instructing witnesses to draw what happened to them
Bibliographic record
Abstract
Abstract We reviewed the child and the adult literature on the impact of witnesses drawing what happened on the number of details recalled and the accuracy of the reported details. Most experiments reported a beneficial effect of drawing what happened (or drawing the scene) on the number of details reported primarily in free recall and sometimes also in cued recall. The consensus across studies was that drawing protocols did not negatively impact the accuracy of reported details (when accuracy was attainable) unless suggestive details were drawn. These results were largely consistent regardless of interviewer expertise or protocol used. Draw‐and‐tell instructions should be considered by forensic investigators for the following reasons: (1) the beneficial effect on number of details recalled with no detriment to accuracy, (2) the added benefit for children who need additional interviewer support, and (3) the ease at which the instruction can be implemented with minimal expertise or training. However, more ecologically valid research is needed to establish the efficacy of drawing (1) in forensic interviews, (2) in the presence of misinformation, (3) across instances of repeated event memory, and (4) across sequential interviews.
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 | 0.011 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".