Dual Language: A Combined Dual Language Framework for Implementation: A 90/10 and 50/50 Model Design
Bibliographic record
Abstract
Dual Language programs are starting to resurface amongst the best practices for increasing literacy and academic language acquisition. Substantial evidence exists to support dual language (DL) education as a viable and enriching method of supporting high levels of academic achievement for both English Language Learners (ELLs) and English-speaking students (as cited in Ray, 2009). With that being said, there is no doubt that a DL program will increase the academic achievement of Culturally Linguistically Diverse (CDL) students. However, the question that arises in the implementation of DL programs is, which model either the 90/10 or the 50/50 is effective in sustaining academic achievement of CLD students during their educational experience? One issue that can impact Dual Language Education (DLE) program design concerns the allocation of time given to each language (Lindholm-Leary, 2012). The purpose of this research is to test the implementation of the conceptual dual language framework developed by the researchers that embraces both the 90/10 and 50/50 model allowing for a blended allocation of time given to each language for biliteracy mastery. Although the focus of the study took place in the United States, the researchers have also reviewed Europe and the Middle East where the Content and Language Integrated Learning (CLIL) model is very popular as other possible contexts for implementation of the framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".