Students’ Source Misuse in Language Classrooms: Sharing Experiences
Bibliographic record
Abstract
In this article we first provide a brief discussion of what is generally referred to as “student plagiarism,” which we prefer to call “source misuse” or “inappropriate textual borrowing,” and then provide some of the factors that may contribute to this problem in language classes. Moreover, we provide our views and sugges- tions on how to deal with these causes, drawing mainly on our experiences as students and teachers in both EFL and ESL contexts, but also making references to some conceptual frames and the related literature.Dans cet article, nous présentons d’abord une discussion brève de ce que l’on appelle communément « le plagiat étudiant » et ce que nous préférons nommer « un mauvais emploi des sources » ou « un emprunt textuel inapproprié ». Nous poursuivons en évoquant quelques-uns des facteurs qui pourraient entrainer ce problème dans les cours de langue. De plus, nous offrons nos avis et nos sugges- tions quant à la façon d’aborder ces causes en puisant surtout dans nos expéri- ences tant comme étudiants qu’enseignants dans des milieux d’ALS et d’ALE, mais également en faisant référence à des cadres conceptuels et à la documentation qui s’y rattache.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".