Error Patterns and Interference Features of French-Speaking Learners of KSL/KFL
Bibliographic record
Abstract
This paper presents the characteristics and causes of Korean errors made by French-Speaking learners of KSL/KFL. We studied a corpus constituted of typical oral and written error sentences produced by 70 students attending the Korean language courses at the University of Montreal in Canada. According to the non-contrastive approach, error is defined as a ‘transitional language’, not as a ‘something wrong’. The errors were classified and analyzed into 8 categories: pronunciation, spelling, particle, word order, verb suffix, tense, politeness and vocabulary. By applying the inference theory of J.C. Richards, we explained what constraints learners would be facing when assimilating the target language and what type of interferences lead them to produce errors. Our findings show that errors represent their transitional language performed at a particular stage of the acquisition and can be caused by interference of their mother tongue and also by interference of Korean development and/or intra-lingual complexity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".