Variability in ESL Outcomes: The Influence of Age on Arrival and Length of Residence on Achievement in High School
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
This article integrates findings from earlier research (Roessingh and Kover, 2003; Roessingh, Kover, and Watt, 2005) linking distinct patterns of achievement for diverse age-on-arrival (AOA) cohorts of ESL learners on the grade 12 Alberta English language arts (ELA) examination to their vocabulary and reading comprehension scores on a standardized measure over time. Recasting the data and conducting simple statistical procedures can offer further insights into the features of cognitive academic language proficiency (CALP): the relationship between vocabulary development and academic performance. I consider ESL program effects and the connection between age on arrival, vocabulary size, and achievement outcomes as reflected on the ELA examination. I compare the ESL students' scores with those of a random sample of their native-speaking (NS) academic counterparts to note patterns among the various cohorts of learners. The results suggest that measures of language proficiency (e.g., vocabulary) can be used to gain direct insights into students' academic achievement. This work has important implications for the development of theoretical growth models that would establish language-learning trajectories of good ESL progress for varied AOA and lengths of residence (LOR) fitted against a NS trajectory.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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