An Exploration of the Relationship between Oral Language Proficiency and the Success of English Language Learners in Reading Recovery
Bibliographic record
Abstract
The diverse population of learners includes students who are high performing in reading as well as those who struggle with reading. This research concerns struggling readers. The goal of teachers is to identify struggling readers and discover ways to address the reading needs of those students. Pinnell (2006) stated that teachers have a common goal: to make literacy a true part of the lives of all students. There are many interventions to help struggling readers. Reading Recovery (RR) is a short-term reading intervention program designed to help the children develop effective strategies for reading and reach average levels for their particular peer group (Fountas & Pinnell, 1996). Research has confirmed the positive impact of RR on readers who struggle (Allington, 2005; Clay, 1993; McKee, 2006; Schwartz, 2005). In particular, Allington (2005) outlined five principles of scientific reading instruction: (a) classroom organization; (b) matching pupils to texts; (c) access to interesting texts, choice, and collaboration; (d) writing and reading; and (e) expert tutoring. Research has shown that RR addresses four of these five principles.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".