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Record W4376616208 · doi:10.1177/10775595231175913

Child Witnesses Productively Respond to “How” Questions About Evaluations but Struggle With Other “How” Questions

2023· article· en· W4376616208 on OpenAlexaff
Hayden Henderson, Colleen E. Sullivan, Breanne E. Wylie, Stacia N. Stolzenberg, Angela D. Evans, Thomas D. Lyon

Bibliographic record

VenueChild Maltreatment · 2023
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBrock University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsPsychologyVerbQuestions and answersDevelopmental psychologyClothingLeading questionSexual abuseSocial psychologyPoison controlSuicide preventionCognitive psychologyMedicineLinguistics

Abstract

fetched live from OpenAlex

Child interviewers are often advised to avoid asking “How” questions, particularly with young children. However, children tend to answer “How” evaluative questions productively (e.g., “How did you feel?”). “How” evaluative questions are phrased as a “How” followed by an auxiliary verb (e.g., “did” or “was”), but so are “How” questions requesting information about method or manner (e.g., “How did he touch you?”), and “How” method/manner questions might be more difficult for children to answer. We examined 458 5- to 17-year-old children questioned about sexual abuse, identified 2485 "How” questions with an auxiliary verb, and classified them as “How” evaluative ( n = 886) or “How” method/manner ( n = 1599). Across age, children gave more productive answers to “How” evaluative questions than “How” method/manner questions. Although even young children responded appropriately to “How” method/manner questions over 80% of the time, specific types of “How” method/manner questions were particularly difficult, including questions regarding clothing, body positioning, and the nature of touch. Children’s difficulties lie in specific combinations of “How” questions and topics, rather than “How” questions in general.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.043
GPT teacher head0.318
Teacher spread0.275 · 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.

Study designBench or experimental
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

Citations5
Published2023
Admission routes1
Has abstractyes

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