MétaCan
Menu
Back to cohort

Linguistic Constraints on Children's Overt Marking of <i>BE</i> by Dialect and Age

2012· article· en· W2007966061 on OpenAlexaff
Joseph Roy, Janna B. Oetting, Christy Wynn Moland

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Ottawa
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsPsychologyLinguisticsPast tenseAmerican EnglishDevelopmental psychologyVerb

Abstract

fetched live from OpenAlex

PURPOSE: Overt marking of BE in nonmainstream adult dialects of English is influenced by a number of linguistic constraints, including the structure's person, number, tense, contractibility, and grammatical function. In the current study, the authors examined the effects of these constraints on overt marking of BE in children as a function of their nonmainstream English dialect and age. METHOD: The data were language samples from 62 children, ages 4-6 years; 24 children spoke African American English (AAE), and 38 spoke Southern White English (SWE). Analyses included analysis of variance and logistic regression. RESULTS: Rates of overt marking varied by the children's dialect but not their age. Although the person, number, tense, and grammatical function of BE influenced the children's rates of marking, the nature and magnitude of the influence differed by the children's dialect. For AAE-speaking children, contractibility also influenced their marking of BE. CONCLUSIONS: Consistent with the adult literature, the current study showed that AAE- and SWE-speaking children marked BE in ways that differed from each other and from what has been documented for child speakers of Mainstream American English. These findings show stability in the use of BE in AAE and SWE that spans different generations and different dialect communities.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 head0.064
GPT teacher head0.405
Teacher spread0.341 · 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 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

Citations25
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Speech Language and Hearing ResearchSame topicLinguistic Variation and MorphologyFrench-language works237,207