Relationship between L1 and L2 word‐level reading and phonological processing in adults learning English as a second language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Word‐level reading and phonological processing measures were administered in English and Chinese to adult ESL students whose first language (L1) was Mandarin and whose second language (L2) was English. Instructors also identified students who may be at risk for L2 reading difficulties based on specific identification criteria. L2 phonological processing measures were related to L2 word‐level reading and there was a cross‐linguistic relationship between L1 and L2 phonological processing measures. Students considered at risk for L2 reading difficulties also differed significantly from those students not at risk on one L1 and several L2 phonological processing measures. Results are discussed in relation to contemporary theory on the assessment and identification of reading difficulties in English language learners.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it