Testing a digital <scp>self‐interview</scp> approach: The virtual memory assistance tool
Bibliographic record
Abstract
Abstract Technology‐mediated interviews are a promising supplement to in‐person interviews for questioning eyewitnesses. We sought to develop and test a virtual self‐administered memory‐elicitation procedure—The virtual memory assistance tool (VMAT). The VMAT is a web‐based memory retrieval tool designed around the principles of the Cognitive Interview. In Experiments 1 and 2, participants (N = 135, N = 127) watched a target video and then received either VMAT or Control instructions, reporting their memory either by Typing into a textbox or Speaking into their device's microphone. In Experiments 3 and 4, participants (N = 89, N = 78) watched a target video and then received either VMAT or Control instructions presented in either Audio or Video format. Our findings suggest that a virtual tool for memory elicitation seems effective independent of Interview Procedures, Response Modalities, and Instruction Modalities and across differing target stimuli (mock‐crime vs. content of a sexual nature).
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".