Analysis of Compositions by B1 Level (Threshold) Francophone Learners of Catalan: Typology of Errors and Correspondences
Bibliographic record
Abstract
The focus of this research is to study the different types of errors made by intermediate level learners of Catalan (Threshold, B1) in written productions. The corpus is made up of compositions written by learners with French as a first language (L1) that were part of the official Catalan language examinations of the Ramon Llull Institute, conducted in Montreal between 2009 and 2016. We systematically identified and analyzed errors in each written text using an ad hoc constructed classification system presented here. Errors were categorized in terms of linguistic description criteria (linguistic aspect and modification type) and etiological criteria (interlinguistic influences and intralinguistic causes). \n \nWith a Multiple Correspondence Analysis (MCA), it was possible to identify associations and association patterns between the characteristics of different variables. For example, we were able to observe the more or less significant impact of certain variables on the occurrence of an error, the most frequent characteristics of certain types of errors, etc. The analysis is supplemented with real examples from our corpus. \n \nThe results we obtained allowed us not only to observe certain types of general phenomena, but also to detect specific recurrent errors from our corpus. This information would be useful to teachers when creating pedagogical activities in their quest to seek more effective ways to support their learners in writing (Arntzen, Håkansson, Hjedle & Keßler, 2019).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".