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Record W2789805158 · doi:10.7202/1050808ar

The Acquisition of Prepositional Meanings in L2 Spanish

2018· article· en· W2789805158 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Applied Linguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImage schemaLinguisticsSchema (genetic algorithms)MetaphorCognitionPsychologyConceptual metaphorSecond-language acquisitionCognitive linguisticsCognitive psychologyCognitive scienceComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

This study examines how adult second language learners acquire the different meanings of the Spanish preposition a. Cognitive linguistic models predict that spatial meanings are acquired first, as they are conceptually basic and are the source from which other meanings derive via natural cognitive mechanisms such as metaphor and image-schema transformation (Tyler & Evans, 2003). However, there has been little empirical evidence to support this hypothesis. Results from an oral story-telling task conducted with beginner (N = 10), intermediate (N = 10), and advanced (N = 4) learners suggest that the acquisition of prepositional meanings is not driven solely by cognitive mechanisms, but rather that other non-conceptual factors, such as collocational patterns, cross-linguistic transfer, frequency, and saliency, also play a prominent role. These findings imply that learners approach the multiple varied meanings of a preposition by relying on several different mechanisms simultaneously.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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