Bibliographic record
Abstract
This volume synthesizes research on the relationships among oral language, literacy, and academic achievement for English language learners (ELLs) in the United States, from pre-Kindergarten through Grade 12. It explores how these findings have been applied in school and classroom settings and recommends areas of focus for future studies in order to improve education for these students. Why is it important to assess what we know about the education of ELLs? The most basic reason, of course, is that we seek to provide, for ALL students, a high quality education that takes into account their individual strengths and needs. The level of academic achievement for students with limited proficiency in English in the United States has lagged significantly behind that of native English speakers. One congressionally mandated study reported that ELLs receive lower grades, are judged by their teachers to have lower academic abilities, and score below their classmates on standardized tests of reading and mathematics (Moss and Puma, 1995). According to a compilation of reports from forty-one state education agencies, only 18.7 percent of students classified as limited English proficient (LEP) met the state norm for reading in English (Kindler, 2002). Furthermore, students from language minority backgrounds have higher dropout rates and are more frequently placed in lower ability groups and academic tracks than language majority students (Bennici and Strang, 1995; President's Advisory Commission on Educational Excellence for Hispanic Americans, 2003; Ruiz-de-Velasco and Fix, 2000). These educational facts intersect with the demographic facts to strengthen the rationale for this research synthesis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.491 | 0.376 |
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".