Learning the Genre “Summary” by Undergraduate French Language Students: A Didactic Device at the Service of Academic Literacy
Bibliographic record
Abstract
In this article, we aim to present the work with the written production in French during a semester of the undergraduate course in French as Foreign Language, seeking to develop the language capacities of students, in LE. More specifically, we will present the work done with students on language operations related to the process of summarizing the ideas of a text (MACHADO, 2010) and the avoidance of repetition, but through a socio-discursive and interactionist perspective, showing the device that we created, the students’ productions and discussing the role of the Academic Literacy Laboratory (LLAC) in developing students’ language capacities. Our study takes as a theoretical and methodological basis Socio-discursive Interactionism (BRONCKART, 1999) and its consequences for the Didactics of Languages, through the concepts of: didactic model (DE PIETRO; SCHNEUWLY, 2003), didactic sequence (DOLZ; NOVERRAZ; SCHNEUWLY, 2004), language capacities (DOLZ; PASQUIER; BRONCKART, 1993). The results pointed out that the students developed the discursive and linguistic-discursive capacities, especially those related to the act of summarize, using nouns and pronouns to avoid repetition. It also shows that the device is relevant to the learning of the genre summary and also of the content of the text that was summarized, what illustrates the perspective of “writing to learn” (GERE, 2019) and the epistemic function of writing.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".