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Record W4405184594 · doi:10.1080/1068316x.2024.2439098

Children’s strategic regulation during a confidence lineup paradigm

2024· article· en· W4405184594 on OpenAlexafffund
Kaila C. Bruer, Heather L. Price

Bibliographic record

VenuePsychology Crime and Law · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsThompson Rivers UniversityUniversity of Regina
FundersSocial Sciences and Humanities Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsPolitical sciencePsychologyLaw and economicsEconomics

Abstract

fetched live from OpenAlex

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, Citation1996; Citation2001). 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., Citation2008) 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.344
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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