Editorial Perspective: Questioning kids: applying the lessons from developmentally sensitive investigative interviewing to the research context
Bibliographic record
Abstract
In 1989, the United Nations Convention on the Rights of the Child solidified that “the child who is capable of forming his or her own views [has] the right to express those views freely in all matters affecting the child.” Involving children as research participants is one key method of ensuring that children’s voices are heard, especially in psychological science where researchers gather information from children across clinical, developmental, health, cognitive, and social research contexts and in related fields like psychiatry, social work, nursing, and medicine. How we talk to children as researchers affects whether we produce rigorous science, and a consensus that has emerged from decades of research on children’s eyewitness testimony demands that we pause for critical self-reflection. In this piece, we propose that scientists studying children consider a large but siloed body of work on developmentally-sensitive investigative interviewing to facilitate complete, accurate, and honest responding from children in research studies. By examining how researchers phrase questions, how they contextualize questioning, and how they report methodology, we address concrete avenues for ensuring robust and reliable psychological science, as well as pathways for children’s optimal involvement in the research process.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.027 | 0.030 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".