Ensuring Proper Competency in the Host Language: Contrasting Formula and the Place of Heritage Languages
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background Context In most immigrant-receiving societies, an important question, both for researchers and policy makers, has been the weighing of the relative efficiency of different formulas in the learning of the host language by immigrant students, especially the potential impact of specific services on social integration and the role of heritage languages. Purpose Objectives Research Question Focus of Study This article tries to go beyond the most conspicuous elements of these controversies to look at the variety of practices that different societies have adopted. Given the questions just raised, a specific focus is given to the degree to which such endeavors follow an immersion or specific services formula on the one hand, and to the role that they grant to heritage languages on the other. Five major immigrant-receiving societies have been chosen, and their choices regarding either issue are contrasted: Britain, two Canadian provinces (Quebec and Ontario), the United States, and Belgium (Flemish Brussels). Research Design To ascertain the extent to which transferable conclusions about best models and practices can be drawn from international comparison, evaluation research on the strengths and weaknesses of each of these formulas is reviewed, with a focus on their short and middle-term linguistic outcomes given the paucity of data on their long-term educational and social outcomes. In conclusion, we identify the minimum threshold of consensus regarding the policy and program conditions that foster a proper mastery of the host language by immigrant students without jeopardizing other dimensions of their school or social integration. Conclusion/Recommendations Three recommendations for policy makers can be drawn. First, flexibility and diversity of formula, both regarding the specific-services-versus-quick-integration dilemma and the place of heritage languages, seems a much better option than the one-size-fits-all model given the great variety found within the immigrant student population. Second, regardless of the model adopted, a fundamental winning condition lies in the recognition that the linguistic integration of newcomers is a collective responsibility and thus necessitates the establishment of close links between specific services, whenever they exist, and regular classrooms. Finally, research points to the necessity of focusing attention on schools and classrooms, especially pedagogical practices and teaching strategies, instead of being obsessed with models and formula.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it