Does eyewitness and interviewer gender influence children’s reports? An experimental analysis of eyewitness and interviewer gender on children’s testimony
Bibliographic record
Abstract
= 127, age = 6-11 years) witnessed an experimenter (E1) find money, which he/she may/may not have taken. E1 then asked the children to falsely deny that the theft occurred, falsely accuse E1 of taking the money, or tell the truth when interviewed by a second experimenter. Falsely denying or falsely accusing influenced children's forthcomingness and quality of their testimony. When accusing, boys were significantly more willing than girls to disclose about the theft earlier and without being asked directly. When truthfully accusing, children gave lengthier testimony to same-gendered adults. When denying, children were significantly more willing to disclose the theft earlier to male interviewers than to females. As children aged, they were significantly less likely to lie, more likely to disclose earlier when accusing, and give lengthier and more consistent testimony.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".