Newcomer Integration and Academic Support in Newfoundland and Labrador
Bibliographic record
Abstract
This article extracts eight points for discussion from many years of research in newcomer academic support and social integration in Newfoundland and Labrador. These points include: transportation to school for newcomer students; resources and support for ESL (English as a Second Language) teachers; coordination of the ESL program; workload and student-teacher ratio in ESL & LEARN (Literacy Enrichment and Academic Readiness for Newcomers) programs; hiring criteria and qualifications for ESL and LEARN positions; assessment of newcomer learners for placement and learning disability; non-ESL teachers’ in-service training on working with newcomer students; and collaboration of educational stakeholders. Some of the points were elaborated in other articles (e.g., Doyle, Li, & Grineva, 2016; Li & Grineva, 2016; Li, Que, & Power, 2017) while others are first-time mentions. The majority of these points were highlighted in the last stage of our previous SSHRC project that focused on best practices and policy recommendations in the education sector. For the purpose of this discussion article, we summarize information from multiple data sources without using direct quotations from the participants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".