The Bridge: From research To PracTice Research on Error Correction and Implications for Classroom Teaching
Bibliographic record
Abstract
A fter three decades, immersion programs are still considered enormously successful with respect to the second language (L2) proficiency levels attained by students enrolled in such programs and their concurrent development of academic skills in both the native and target languages. Indeed, immersion has evolved in some cases beyond the program types that originated in the Canadian context and is now being applied in a wide range of situations and at multiple levels with differing goals, socioeconomic and cultural contexts, and methods of implementation (Swain & Johnson, 1997). Yet research conducted since the late 1970s has firmly established that immersion students ’ L2 productive skills are not on a par with those of their native-speaking counterparts. In other words, immersion students do not attain native-like proficiency in speaking and writing. The reasons for this phenomenon are many and varied, but some are related to instructional issues. Most immersion teachers tend to focus their attention on the instruction of subject matter content; academic achievement usually receives increased emphasis because of school district expectations and parental concerns. Yet “...subject-matter teaching does not on its own provide adequate language teaching ” (Lyster and Ranta, 1997, p. 41). It has also been observed that lack of systematic approaches for teaching specific language structures in meaningful contexts and for attending to student errors contribute to less than optimal levels of proficiency in immersion students (e.g., Chaudron, 1986; Harley, 1989; Kowal and
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".