Urgent issues and prospects on investigative interviews with children and adolescents
Bibliographic record
Abstract
Abstract While there has been considerable research on investigative interviews with children over the last three decades, there remains much to learn. The aim of this paper was to identify some of the issues and prospects for future scientific study that most urgently need to be addressed. Across 10 commentaries, leading scholars and practitioners highlight areas where additional research is needed on investigative interview practices with youths. Overarching themes include the need for better understanding of rapport‐building and its impact, as well as greater focus on social‐cultural and developmental factors and the needs of adolescents. There are calls to examine how interviews are occurring in real‐world contexts to better inform best practice recommendations in the field, to find means for ensuring better adherence to best practices among various groups of practitioners, and to understand their importance and impact when not followed, including by those testifying in courts. All reflect the need to better address that recurring challenge of reliably and consistently eliciting accurate and credible information from potentially reluctant young witnesses.
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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 | 0.359 | 0.402 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".