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Record W2107412196 · doi:10.1017/s0305000909990146

Gender-marked determiners help Dutch learners' word recognition when gender information itself does not

2010· article· en· W2107412196 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

VenueJournal of Child Language · 2010
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsAmorfix (Canada)University of Toronto
Fundersnot available
KeywordsPsychologyNonsenseGrammatical genderLinguisticsSentenceComprehensionNounDeterminerLanguage acquisitionMathematics education

Abstract

fetched live from OpenAlex

Dutch, unlike English, contains two gender-marked forms of the definite article. Does the presence of multiple definite article forms lead Dutch learners to be delayed relative to English learners in the acquisition of their determiner system? Using the Preferential Looking Procedure, we found that Dutch-learning children aged 1 ; 7 to 2 ; 0 use articles during sentence comprehension in a fashion comparable to similarly aged English learners. That is, Dutch learners' sentence processing was impaired when a nonsense (se) as opposed to real article (de, het) preceded target words, much like English learners' sentence processing is disrupted by the use of a nonsense article. At the same time, however, gender cues did not help Dutch learners recognize target nouns more efficiently, indicating that gender has yet to be acquired. Thus, although Dutch-learning children aged 1 ; 7 to 2 ; 0 have not mastered all aspects of their language's article system, they nonetheless use their partial knowledge of articles during speech processing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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