A Functional Approach to Research on Content-based Language Learning: Recasts in Causal Explanations
Bibliographic record
Abstract
There is wide agreement among researchers that content-based language learning (CBLL) instruction is most effective when it provides both meaningful communication about content and intentional language development (e.g., Pica, 2000). However, it is less widely recognized that a systemic functional linguistic (SFL) approach offers a distinctive theoretical perspective and characterization of CBLL and addresses issues of advanced language development which are crucial when the second language is a medium of learning. To demonstrate this, we analyze the grammatical scaffolding by teacher and L2 learner(s) of causal explanations which form part of work by a group of L2 students in a project on the human brain. We show how an SFL analysis reveals quite different aspects of the recast sequences of these data than does a 'focus on form' approach. These aspects include: the lexicogrammar of causal meanings, the place of 'grammatical metaphor' in the processes of language development, the nature of causal explanations as knowledge structures of 'ideational meaning' in discourse, and the role of knowledge structures as bridges between language learning and content learning. The potential of the functional perspective to increase the range and power of research on CBLL considerably is thus seen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".