Unfolding vision: English language learning supports in a small school division.
Bibliographic record
Abstract
English language learning (ELL) students (n=24), their parents (n=25), teachers (n=18) and school administrators (n=7) (total n=74) took part in this study to contribute to a better understanding of the educational, cultural and social supports of ELL students in the Grande Prairie Public School Division (GPPSD). The four groups filled out a survey and participated in focus group discussions to identify the needs of the ELL students and describe the existing and recommended supports for the ELL students. Responses revealed that there is a diverse range of ELL students from various cultural backgrounds attending GPPSD. At the time of the study, the participants indicated that the integration model (the classroom teacher as the main support along with differentiated assignments and peer-partner supports) and the pullout model of instruction were supporting the ELL students in the classroom. Based on a comparison of participant responses with promising ELL practices from a literature review, the author recommended a comprehensive ELL program with 1) open and continuous dialogue between teachers and administrators, 2) collaboration and networking with community groups, 3) an initial welcoming intake and orientation procedure, 4) trained ELL designated coordinator and support teachers, 5) family literacy programming and fostering of ELL student's first language, 6) encouragement for cultural awareness and district-wide professional learning. A comprehensive program would encourage socialization of the ELL students through peer supports, after school and summer ELL classes, thus strengthening the integration model of support. --P. ii.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".