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Heritage Language Learners and Automaticity: The Use of "por" and "para"

2013· article· en· W577000217 on OpenAlexvenueno aff
Valerie J. Trujillo

Bibliographic record

VenueEntrehojas Revista de Estudios Hispánicos · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsAutomaticityGrammaticalityPsychologyTask (project management)LinguisticsSecond languageDescriptive knowledgeFocus on formProcedural knowledgeHeritage languageCognitive psychologyPedagogyComputer scienceGrammarCognitionKnowledge baseArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the use of the Spanish prepositions por and para (P&P) by heritage-language (HL) learners to analyze whether they demonstrate automaticity in their application, as is common with L1 Spanish speakers (i.e. “it just sounds right”), or if they rely on the conscious, declarative knowledge of the prescriptive uses of these prepositions, as is common with speakers for whom Spanish is an L2. Upper and lower HL learners and upper and lower FL learners (non-HL learners) were asked to complete a cloze test and a grammaticality judgment task and were asked to explain their judgments. The explanations given by students provide a glimpse into the type of knowledge, whether declarative or procedural, that students tap into when using P&P. This study found that while FL learners relied on declarative knowledge in the application of P&P, HL learners demonstrated a level of automaticity and procedural knowledge in their use of these prepositions. This suggests that HL learners have internalized the uses of P&P though communicative exposure throughout their childhood, which may be more effective than the explicit instruction FL learners have received on these structures. This study aims to add to the growing body of literature on heritage language processes and development.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.064
GPT teacher head0.376
Teacher spread0.313 · 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 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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