Social perspectives on interaction and language learning in CLIL classrooms
Bibliographic record
Abstract
Book synopsis: Studies on discourse and language learning originated in the field of general education and they focused on first language learning environments. However, since 1980s research on discourse and language learning broadened the scope of investigation to respond to second and foreign language environments. Recently, the emergence of new language learning contexts such as computer mediated communication, multilingual settings or content and language integrated contexts requires further research that focuses on discourse and language learning. From this perspective, the present volume aims to broaden the scope of investigation in foreign language contexts by exploring discourse patterns in the classroom and examining the impact of factors such as gender, explicitness of feedback or L1 use on language learning through discourse. \n \nWith that aim in mind, this volume will bring together research that investigates discourse in various instructional settings, namely those of primary, secondary and university L2 learning environments, content and language integrated contexts and other new language learning settings. The number and variety of languages involved both as the first language (e.g. English, Finnish, Basque, Spanish, Japanese, French, Italian, Catalan) as well as the target foreign language (e.g. English, French, Italian, Japanese, Spanish) makes the volume specially attractive. Additionally, the different approaches adopted by the researchers participating in this volume, such as information processing, sociocultural theory, or conversation analysis, widen the realm of investigation on discourse and language learning. Finally, the strength of the volume also lies in the range of educational settings (primary, secondary and tertiary education) and the worldwide representation of contributors across seven different countries, namely those of Spain, France, Austria, Finland, Germany, Canada, Australia and the United States. \n \nThe uniqueness of the volume is due to its eclectic and comprehensive nature in tackling instructional discourse. Worldwide outstanding researchers, like Julianne House, Carme Muñoz, Ute Smit, Tarja Nikula or Roy Lyster, to quote but a few, adopt different perspectives in this joint contribution that will certainly broaden the scope of research on language learners’ discourse.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".