“Just to Jog My Memory”: An Examination of Forensic Interviewers’ Note-taking Behaviors and Perceptions of Notes With Child Witnesses
Bibliographic record
Abstract
= 137) on their note-taking practices, perceptions of note-taking, and note-taking training. Many forensic interviewers surveyed (81%) reported that they take notes during forensic interviews. Of those, the most common reason for note-taking was to assist with remembering what the interviewee reported during the interview (89%) and to guide the formulation of follow-up questions (87%). Note-taking style was also reported upon, with most respondents indicating that they write down keywords that may be used again in the interview (78%), as well as short utterances or sentences related to the presenting narrative (61%). Finally, the majority (50%) of respondents who take notes reported always taking notes, although 29% reported taking notes most of the time. Of those respondents who reported not taking notes during forensic interviews, the majority listed the reasons as being that it distracts the child from the interview (85%) and causes them to break eye contact with the child (46%). Overall, many respondents endorsed the benefits of note-taking to the interviewing process, whereas a small minority reported some perceived risks or concerns with note-taking during interviews. Perhaps most notably, forensic interviewers, both of whom take notes and those who do not, reported low rates of note-taking training and a desire for more information on note-taking practices within the field. These results underscore the need for further research and best practice guidelines regarding note-taking during forensic interviews.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".