Sociocultural Competence Formation in Students of Linguistic Universities When Working with a Poetic Text in French as a Part of Analytical Reading Teaching (by the Example of Émile Nelligan’s Poem “Winter Evening”)
Bibliographic record
Abstract
The study aims to present a scientifically grounded set of techniques and exercises for sociocultural competence formation in philology students when teaching analytical reading of a foreign-language poetic text from the perspective of a competence approach, which contributes to the expansion of students’ vocabulary, mastery of the basics of foreign-language poetic text analysis, involves dictionary work, including independent work. The set of techniques and exercises presented in the paper was developed by its author on the basis of “Winter Evening” (“Soir d’hiver”), an authentic text by the Canadian poet of the XX century Émile Nelligan, and can be successfully used to teach analytical reading to university students as a part of familiarisation with the culture of a target-language country and the mentality of native speakers of the target language. It is important to note that sociocultural competence is an obligatory component of communicative competence and is directed at avoiding misunderstandings in interpersonal communication. Currently, the term “sociocultural competence” still requires clarification of the essence of the concept. Scientific novelty of the study lies in identifying a list of skills, techniques, exercises aimed at sociocultural competence formation in philology students. As a result, it has been proved that the use of the set of techniques and exercises contributes to acquisition of linguistic and sociocultural knowledge about target-language countries by students of a linguistic university, as well as practical mastery of the French language in the process of teaching analytical reading to students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".