Résumés de texte en langue maternelle et en langue seconde : Différences dans l'application des macrorègles entre experts et étudiants de différents niveaux universitaires
Bibliographic record
Abstract
This study analyzes the differences between experts and university students of various academic levels in the application of the macrorules of deletion, generalization and construction on text summaries. Learners of French as a second language were required to write a summary of a French text in French, as well as an English text in English, their mother tongue, whereas professors of French and English were asked to do the same thing but only in their mother tongue. Analysis of the results showed that, with regard to the Deletion rule, there are significant differences between French learners and French experts as well as between English experts and the same students. As for the generalization rule, significant differences are also observed between learners of French and French experts, but none when these students are compared to English experts. Similar results are found for the construction rule. Level of proficiency in French has an influence on the application of some rules. Explanations follow and pedagogical suggestions are offered.
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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 | 0.002 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".