Children’s strategic regulation during a confidence lineup paradigm
Bibliographic record
Abstract
The accuracy of memory reports is dependent, at least in part, on a person’s ability to screen out incorrect responses by engaging in strategic regulation of memory reports (Koriat & Goldsmith, 1996; 2001). One way in which strategic regulation can be examined is through the use of an explicit ‘I don’t know’ (IDK) response option during memory report tasks. The present research explored the extent to which a confidence identification paradigm (see Sauer et al., 2008) encouraged strategic regulation in child eyewitnesses when reporting on their recognition memory. We recruited 545 children (aged 6-11) who were assigned to (1) a confidence paradigm that either contained or did not contain an explicit ‘I don’t know’ (IDK) response option or (2) a traditional lineup paradigm that either contained or did not contain an IDK response option. Regardless of whether an IDK response option was available, the confidence paradigm performed with similar accuracy. However, the IDK option was used more frequently in the traditional lineup paradigm than in the confidence paradigm. Taken together, these findings suggest that the confidence identification paradigm sufficiently encourages strategic regulation in children when reporting on recognition memory without the need for an explicit no-response option.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".