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Record W1964928248 · doi:10.1075/eurosla.6.06bor

Specificity in Spanish

2006· article· en· W1964928248 on OpenAlexaff
Claudia Borgonovo, Joyce Bruhn de Garavito, Pedro Guijarro‐Fuentes, Philippe Prévost, Elena Valenzuela

Bibliographic record

VenueEUROSLA Yearbook · 2006
Typearticle
Languageen
FieldComputer Science
TopicLinguistic Studies and Language Acquisition
Canadian institutionsWestern UniversityUniversité Laval
Fundersnot available
KeywordsSyntaxLinguisticsPragmaticsSecond-language acquisitionSemantics (computer science)Contrast (vision)PortugueseInterpretation (philosophy)Computer scienceBrazilian PortugueseInterface (matter)PsychologySemantic interpretationNatural language processingArtificial intelligencePhilosophyProgramming language

Abstract

fetched live from OpenAlex

Recent proposals argue that interface areas such as syntax/semantics and syntax/pragmatics are particularly difficult for adult learners, in comparison to purely syntactic phenomena (Sorace 2003, 2004). In contrast, other research shows that L2 learners are able to acquire target representations even when the interpretation is not readily available in the input (Borgonovo, Bruhn de Garavito and Prévost 2005, Dekydtspotter and Sprouse 2001). In this paper we add to the growing literature on the acquisition of interpretational properties by showing that adult L2 learners can acquire knowledge of the syntactic correlates of the semantic notion of specificity in constructions involving topicalisation and null objects in Spanish. The learners’ first language (L1) is Brazilian Portuguese, where specificity does not play the role in these constructions that it plays in Spanish. Results show that learners can go beyond their L1 with respect to the acquisition of interface phenomena, suggesting that native-like grammars are attainable in L2 acquisition.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations19
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueEUROSLA YearbookSame topicLinguistic Studies and Language AcquisitionFrench-language works237,207