The Griffiths Question Map: A Forensic Tool For Expert Witnesses’ Assessments of Witnesses and Victims’ Statements
Bibliographic record
Abstract
Expert witnesses are sometimes asked to assess the reliability of young witnesses and victims' statements because of their high susceptibility to memory biases. This technical note aims to highlight the relevance of the Griffiths Question Map (GQM) as a professional forensic tool to improve expert witnesses' assessments of young witnesses and victims' testimonies. To do so, this innovative question type assessment grid was used to proceed to an in-depth analysis of the interview of an alleged 13-year-old victim of a sexual assault and two rapes. Overall, the GQM stressed how the interview was mainly conducted in an inappropriate manner. The results are examined with regard to scientific knowledge on young witnesses and victims' memory. Finally, it is argued that expert witnesses in inquisitorial systems might use the GQM while encountering difficulties to fulfill the legal standards for expert evidence in adversarial systems because of the lack of studies regarding its reliability.
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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 | 0.026 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".