Classifying California’s English Learners: Is the CELDT too Blunt an Instrument?
Bibliographic record
Abstract
Executive SummaryThere are 1.6 million English learners (ELs) in California’s K-12 public schools, comprising a quarter of California public school students and thirty percent of EL students in the United States. Our study provides strong evidence that California school districts are misidentifying large numbers of entering kindergarten students as English learners. California’s home language survey over identifies children to be administered the California English Language Development Test (CELDT). Because only about 94 percent of kindergarten students taking the CELDT in 2009-10 were classified English language proficient, being identified to take the CELDT almost guarantees a student’s classification as EL. Our findings call into question the validity of the home language survey and the CELDT as the tools for identifying EL students in California.EL misidentification is important because it means that these students are not receiving the language support and education that is appropriate to their language skills. In addition, in an era of budget crises, it becomes especially vital that scarce language development resources be targeted as effectively as possible. The wide net currently being cast by California’s EL classification system in some ways renders the classification itself meaningless, given its application to such a wide range of students. Part of the problem is that there is no clear definition of what constitutes “an English language learner” (Abedi 2008, Abedi & Gándara 2006). That definition is left to district interpretation, resulting in significant variability in classification criteria and rates across the state.
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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 | 0.037 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".