Conversational Style and Early Academic Language Skills in CLIL and Non-CLIL Settings: A Multilingual Sociopragmatic Perspective
Bibliographic record
Abstract
As academic language skills develop, young learners are able to rise to the challenge of increasingly complex communication in increasingly formal settings (Snow, 2014; Uccelli et al., 2015). Studies suggest that CLIL contexts may favour the development of academic language skills (Dalton-Puffer, 2007; Nikula, 2007; Marsh, 2008; Pasqual Peña, 2010) to a greater extent than non-CLIL contexts. However, research that attempts to test this assumption has so far tended to do so from a pragmalinguistic perspective (Lorenzo & Rodríguez, 2014; Lorenzo, 2017). This paper takes a sociopragmatic approach to exploring the differences between CLIL and non-CLIL contexts regarding how they facilitate the development of early academic language skills. That is, how the communicative intentions that underlie CLIL and non-CLIL classroom discourse may help or hinder the development of such skills. The data were collected by observing classroom discourse in CLIL and EFL primary-school lessons, in Spanish-based and Catalan-based linguistic models. The method followed was to apply a taxonomy of the sociopragmatic level of academic language (Henrichs, 2010) to determine the quality of the conversational style and intersubjective cooperation found in the discourse. The results indicate that CLIL classroom discourse is characterised by the sort of conversational style that facilitates the development of academic language skills. However, in terms of intersubjective cooperation the results are somewhat inconclusive. Based on these results, the study suggests raising awareness of the role of conversational style in classroom discourse so as to boost the quality of teacher-student interactions in primary-school CLIL contexts and, thus, contribute to an identified need for continuous improvement of CLIL pedagogies and teacher training (Lorenzo, 2007; de Graaff et al., 2007).
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".