First‐grade latino students' english‐reading growth in all‐english classrooms
Bibliographic record
Abstract
Two main questions were addressed in this study: (1) How does first‐grade Latino English‐language learners’ growth in English instructional reading level and selected word‐level reading subprocesses (ability to read words in isolation, phonemic awareness, and phonics) compare to their monolingual native‐English‐speaking peers’ growth?; and (2) Does first‐grade Latino English‐language learners’ English reading growth (instructional reading level and selected word‐level reading subprocesses) vary according to their oral English language abilities? Participants were 47 students in two first‐grade classrooms—28 were Latino English‐language learners, and 19 were monolingual native‐English speakers. At each of two points in time—mid‐year and end‐of‐year—three reading measures were administered to all participants and an additional four oral‐English measures were administered to the Latino participants. To address the first research question, repeated measures analyses of variance were performed, first using Instructional Reading Level as the dependent variable, then with follow‐up analyses to examine growth in word‐level sub‐processes of reading. The second research question was addressed using repeated measures analyses of covariance. Main findings were that language status (Latino English learners versus monolingual native‐English speakers) was not related to Instructional Reading growth or growth in word‐level subprocesses of reading, and Overall English Oral Ability was not related to Instructional Reading Level growth, but was related to word‐level reading sub‐processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".