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Record W2068202886 · doi:10.1207/s15327817la0904_02

VP-Ellipsis and Anaphora in Child Language Acquisition

2001· article· en· W2068202886 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage Acquisition · 2001
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Ottawa
FundersMax-Planck-Gesellschaft
KeywordsEllipsis (linguistics)Anaphora (linguistics)LearnabilityLinguisticsComputer sciencePhraseGrammarVerb phrase ellipsisNatural language processingArtificial intelligenceVerbPhilosophyResolution (logic)

Abstract

fetched live from OpenAlex

In this article, we report on experiments investigating children’s knowledge of the constraints on ellipsis constructions in English, focusing on subtle contrasts between verb phrase–ellipsis (VPE) and VP-anaphora (VPA). These contrasts are of theoreti-cal interest insofar as they present an apparent learnability paradox: On one hand, the negative constraints on these constructions are underdetermined by the positive evi-dence available to the learner (Crain (1991)); on the other hand, languages systemati-cally vary in the availability of VPE. Hence, it seems as if the constraints cannot be directly encoded in Universal Grammar. Our results—from 2 parallel experiments employing the same stimuli but with different methodologies—show that young chil-dren can correctly distinguish VPE from VPA, respecting the constraints on each.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.700
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.005
GPT teacher head0.249
Teacher spread0.244 · 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