Reframing Confidence Instructions to Child Eyewitness Reduces Overconfidence but Does Not Improve Confidence–Accuracy Calibration
Bibliographic record
Abstract
ABSTRACT Children are well‐documented to exhibit poor confidence–accuracy calibration on lineup identification tasks. Children tend to report overconfidence in their (often inaccurate) lineup identification decisions. This research explored the extent to which school‐aged children's ( N = 142; 6‐ to 8‐year‐old) confidence reports are implicitly driven by perceived social pressure to provide a specific confidence rating. Children were randomly assigned to two different confidence instruction conditions: the neutral ( n = 69) or the reframed conditions ( n = 73). The reframed instructions encouraged honesty and instructed children to ignore perceived pressure when reporting confidence. Results revealed that the reframed instructions resulted in more conservative confidence judgments; however, this shift did not translate into those confidence ratings better reflecting children's identification accuracy. Overall, these findings provide evidence that, while external or social factors play a contributing role, other aspects of development are likely contributing more to the poor confidence–accuracy calibration observed with child eyewitnesses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".