MétaCan
Menu
Back to cohort
Record W2171906515 · doi:10.1002/bsl.2142

Young Children's Difficulty with Indirect Speech Acts: Implications for Questioning Child Witnesses

2014· article· en· W2171906515 on OpenAlexafffund
Angela D. Evans, Stacia N. Stolzenberg, Kang Lee, Thomas D. Lyon

Bibliographic record

VenueBehavioral Sciences & the Law · 2014
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of TorontoBrock University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyDevelopmental psychologyHuman factors and ergonomicsIndirect speechControl (management)Injury preventionSuicide preventionPoison controlSocial psychologyMedicineComputer scienceMedical emergencyLinguistics

Abstract

fetched live from OpenAlex

Prior research suggests that infelicitous choice of questions can significantly underestimate children's actual abilities, independently of suggestiveness. One possibly difficult question type is indirect speech acts such as "Do you know..." questions (DYK, e.g., "Do you know where it happened?"). These questions directly ask if respondents know, while indirectly asking what respondents know. If respondents answer "yes," but fail to elaborate, they are either ignoring or failing to recognize the indirect question (known as pragmatic failure). Two studies examined the effect of indirect speech acts on maltreated and non-maltreated 2- to 7-year-olds' post-event interview responses. Children were read a story and later interviewed using DYK and Wh- questions. Additionally, children completed a series of executive functioning tasks. Both studies revealed that using DYK questions increased the chances of pragmatic failure, particularly for younger children and those with lower inhibitory control skills.

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 imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.105
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.333
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations36
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueBehavioral Sciences & the LawSame topicChild and Animal Learning DevelopmentFrench-language works237,207