Investigating Linguistically Diverse Adolescents’ Literacy Trajectories Using Latent Transition Modeling
Bibliographic record
Abstract
Abstract This study examined the literacy profiles of students from diverse home‐language backgrounds and tracked those profiles from grade 6 to grade 10. The authors also investigated the predictive relations of students’ immigration background, gender, and participation in two instructional programs. The results from latent class and latent transition analyses suggest that grade 6 literacy profiles are strong predictors of literacy profiles in grade 10. Students from diverse home‐language backgrounds and those who had immigrated to Canada tended to have strong literacy profiles and positive trajectories. The analyses also indicate that students who used very little or no English at home, even those who had a strong literacy skill profile in grade 6, may benefit from additional literacy support in high school. In terms of instructional programming variables, participation in an English as a Second Language program was associated with little change in students’ literacy profiles over time, and career‐oriented streaming showed a strong negative impact on literacy skill development. In terms of language‐in‐education policy and practice, the findings support the idea that, generally speaking, students from multilingual home‐language environments retain strong literacy profiles between elementary and high school. The findings also emphasize the importance of quality instructional programming.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".