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Record W2021739945 · doi:10.1353/cjl.2010.0000

In the wrong mood at the right time: Children's acquisition of the Spanish subjunctive in temporal clauses

2010· article· en· W2021739945 on OpenAlexaff
Jeannette Sánchez‐Naranjo, Ana Teresa Pérez‐Leroux

Bibliographic record

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2010
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyLinguisticsPast tenseTask (project management)CognitionSelection (genetic algorithm)MoodBootstrapping (finance)MorphemeCognitive psychologyComputer scienceArtificial intelligenceVerbSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

This study investigates the complexity of the mapping task in children's acquisition of the Spanish subjunctive in temporal clauses. We consider that children's difficulty with this task arises from the interaction between mood and other elements determining the evaluation of the temporal clause, such as semantic factors, tense, and cognition. Forty monolingual Spanish-speaking children first completed a cognitive assessment test, evaluating false belief understanding; this was followed by a linguistic prerequisite test assessing understanding of temporal connectors and knowledge of subjunctive morphology, and finally a temporal clause production task. Results reveal that mood selection in temporal clauses does not simply start with indicative followed by its replacement by subjunctive. On the contrary, the use of subjunctive temporal clauses involves a complex process for children in which tense corresponds to a fundamental source of bootstrapping. These results confirm the view that the acquisition of mood selection undergoes a protracted development. Spanish subjunctive meanings are not immediately accessible to children.

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)0.007
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 score0.787
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.235
Teacher spread0.230 · 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

Citations16
Published2010
Admission routes1
Has abstractyes

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