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Record W2255647364

불어권 캐나다인 한국어 습득과정에서 나타나는 오류 양상

2007· article· ko· W2255647364 on OpenAlexaboutno aff
임성숙

Bibliographic record

Venue코기토 · 2007
Typearticle
Languageko
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsComputer scienceInterlanguagePronunciationFirst languageLanguage transferVocabularyForeign languageSentenceNatural language processingSpellingArtificial intelligenceComprehension approachNatural language
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the characteristics and causes of Korean sentence errors frequently committed by French Canadian learners in their acquisition process of Korean as a foreign language. The error analysis is conducted on the basis of the corpus constituted from typical erroneous oral and written sentences made by the 70 students attending Korean language courses at the University of Montreal in Canada from 1998 to 2005. For the analysis of our corpus, error is defined as a “interlanguage” and “transitional language”, not as a wrong and undesirable language to criticize. The error being a part of foreign language learning process is usually accountable. Thuㅎs the error analysis allows us to describe and explain what kind of constraints would be facing the learners to assimilate the targeted foreign language and which interferences drag the learners to produce errors in pronunciation, spelling, order of words, verb suffix, politeness, vocabulary use in oral and written Korean phrase construction. We examine and apply interference theory of J. C. Richard focusing on three interference features causing error: interference by mother tongue, interference by intra-lingual complexity of the target language and interference by learner’s practical development. As the interferences constitute causes of errors, they are also supposed to give tools to improve didactical method adapting to the learners’ linguistic profile. For future research it is recommended to explore new avenue of teaching method on the basis of our analysis output.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.019
GPT teacher head0.330
Teacher spread0.310 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2007
Admission routes1
Has abstractyes

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