Balancing Content and Language in Instruction: The Experience of Immersion Teachers
Bibliographic record
Abstract
Research on immersion teaching has consistently shown that immersion teachers tend to focus on subject matter content at the expense of language teaching. The response to that research has often entailed suggestions for teachers on how better to integrate language and content in their instruction. However, missing from the discussion are rich descriptions of the actual experiences that immersion teachers have as they attempt to balance language and content in their teaching. This phenomenological study aims to address this gap by exploring teachers’ lived experience with content and language integration. In this article, authors report on findings suggesting that immersion teachers’ experience with balancing language and content is a multifaceted struggle involving issues related to teacher identity, stakeholder expectations, and understandings regarding the relationship between language and content. Implications for school‐based support for immersion programs as well as calls for reform in immersion teacher preparation and professional development are shared.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".