MétaCan
Menu
Back to cohort
Record W2037894182 · doi:10.1177/014272370002006004

Comprehension of 'because' and 'so': the role of prior event representation

2000· article· en· W2037894182 on OpenAlexaff
Judith R. Johnston, Elizabeth Welsh

Bibliographic record

VenueFirst Language · 2000
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComprehensionMeaning (existential)Event (particle physics)PsychologyRepresentation (politics)Developmental psychologyContent (measure theory)LinguisticsCognitive psychologyMathematics

Abstract

fetched live from OpenAlex

This study investigates the role of content familiarity in the comprehension of relational terms. Children aged 3;3 to 5;8 listened to stories and were asked to complete statements containing 'because' or 'so'. Half the stories concerned event sequences that were familiar to the children, and half were accompanied by pictures. When the story content was familiar, half the 3-year-olds and all of the remaining children demonstrated an understanding of at least one term. Half of the 4- and 5-year- olds succeeded with both. Pictures had little effect. These findings suggest that young children know the meaning of 'because' and 'so', and that later development involves changes in general language competencies such as the ability to create novel event representations from language input.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0000.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.274
Teacher spread0.254 · 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

Citations12
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueFirst LanguageSame topicLanguage, Discourse, Communication StrategiesFrench-language works237,207