MétaCan
Menu
Back to cohort
Record W4297984017 · doi:10.5070/g6011120

Children’s acquisition of new/given markers in English, Hindi, Mandinka and Spanish: Exploring the effect of optionality during grammaticalization

2022· article· en· W4297984017 on OpenAlexaff
Vishakha Shukla, Madeleine Long, Paula Rubio‐Fernández

Bibliographic record

VenueGlossa Psycholinguistics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsYork University
FundersNorges Forskningsråd
KeywordsHindiGrammaticalizationNarrativeLinguisticsNumeral systemPsychologyTask (project management)Focus (optics)Discourse markerComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We investigated the effect of optionality on the acquisition of new/given markers, with a special focus on grammaticalization as a stage of optional use of the emerging form. To this end, we conducted a narrative-elicitation task with 5-year-old children and adults across four typologically-distinct languages with different new/given markers: English, Hindi, Mandinka and Spanish. Our starting assumption was that the Hindi numeral ‘ek’ (one) is developing into an indefinite article, which should delay children’s acquisition because of its optional use to introduce discourse referents. Supporting the Optionality Hypothesis, Experiment 1 revealed that obligatory markers are acquired earlier than optional markers. Experiment 2 focused on Hindi and showed that 10-year-old children’s use of ‘ek’ to introduce discourse characters was higher than 5-year-olds’ and comparable to adults’, replicating this pattern of results in two different cities in Northern India. Lastly, a follow-up study showed that Mandinka-speaking children and adults made use of all available discourse markers when tested on a familiar story, rather than with pictorial prompts, highlighting the importance of using culturally-appropriate methods of narrative elicitation in cross-linguistic research. We conclude by discussing the implications of article grammaticalization for common ground management in a speech community.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.259
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.039
GPT teacher head0.379
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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGlossa PsycholinguisticsSame topicMultilingual Education and PolicyFrench-language works237,207