THE BRIDGE: FROM RESEARCH TO PRACTICE Using English Achievement Data to Promote Immersion Education
Bibliographic record
Abstract
ore than thirty years of language immersion instruction in Cana-da and the United States have produced an impressive body of re-search demonstrating its benefits to stu-dents. This research indicates that students gain proficiency in a second language, develop cultural awareness, and perform as well as or better than their non-immersion peers on English proficiency tests (Rubio, 1998). As a result of this success, new im-mersion programs are started every year. Today well over 300,000 students in Canada are enrolled in partial or full-im-mersion programs. In the United States, more than 200 K-12 immersion pro-grams have been started since the first U.S. immersion program began in 1971. Still, the 40,000 students enrolled in U.S. immersion programs represent less than one percent of the total students enrolled in public schools in the United States (Rhodes & Lynch, 1997). It is clear that the immersion concept remains rel-atively unknown in this country. Even in districts that currently have immersion programs, much teacher and administrator time is spent explaining and justifying the program’s aims and philosophy. With the increasing empha-sis on students ’ achievement in the basic skills of math, reading, and writing, along with the development of state standards, second language instruction is not always seen as a top priority by district and state personnel. Although some forward-thinking states in the nation have includ-ed foreign language in the core curriculum, in the state of Minnesota, “World Languages ” is the only optional area of the ten areas of learning required as part of the State Preparatory Standards for students in grades K-8 (Minnesota
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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 | 0.386 | 0.465 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".