The Role of Linguistic Course Concentration in Secondary English Learners’ Attainment: Intersections of School Context and Student Characteristics
Bibliographic record
Abstract
Course-level concentration of English learners (ELs), or the clustering of ELs into courses away from non-ELs, is an underexamined component of curricular tracking at the secondary level. Using data from three ninth grade cohorts (2013–2015) in the New York City Public Schools (NYCPS), as well as data from the American Community Survey and National Student Clearinghouse, this study examines the relationship between course concentration of high school ELs—as measured by the percent of ELs in content courses—and four key outcomes: four- and six-year high school graduation, and immediate and extended enrollment in college. Guided by an ecological framework, we distinguished between schools’ general tendency to concentrate ELs into separate courses and the individual students’ experiences of relative concentration within their schools. We estimated the role of both components of course concentration in two different types of high schools: comprehensive schools and newcomer-serving schools. We found that both components had significant negative associations with high school graduation and college enrollment, though with some notable differences by subgroup and school type. Our findings challenge the common practice of grouping ELs together for instruction but also point to important variations in how course concentration might differentially shape attainment outcomes in different high school contexts.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".