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Record W2892293809 · doi:10.15446/fyf.v31n2.74658

Errores y causas: usos de ser y estar en estudiantes francófonos de ELE

2018· article· es· W2892293809 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueForma y Función · 2018
Typearticle
Languagees
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Este trabajo se centra en el uso de los verbos ser y estar por parte de los estudiantes francófonos de español como lengua extranjera (ele) en Quebec. Utilizar correctamente estos verbos constituye una cuestión difícil de sortear para los estudiantes que poseen un sistema verbal atributivo diferente al del español, debido a la incidencia de factores gramaticales, léxico-semánticos y pragmáticos, así como al carácter cambiante de la lengua. Este artículo presenta un diagnóstico del proceso de aprendizaje de este rasgo gramatical específico y prueba la utilidad del análisis de errores como línea de investigación. Demuestra que la observación de la interlengua de los aprendices y el análisis de sus producciones, permiten establecer un panorama de las dificultades, encontrar sus causas y comprender el fenómeno de la interlengua. Con todo, nuestros resultados indican que la gran mayoría de los errores son de tipo intralingüístico: se encuentran en el sistema del español.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.338
Teacher spread0.324 · 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