“Languages are not the barriers”: Learning together through multilingual cross-curricular poetry writing in the ESL classroom
Bibliographic record
Abstract
The evolving linguistic landscape in 21st century classrooms necessitates a re-evaluation of pedagogical approaches, exploring the potential of multilingual writing techniques within TESOL settings. This article draws on my self-study as a TESOL educator navigating contexts and shifting from an English-only approach in the classroom to an openness of language(s) approach (Ortega, 2019). Following Hamilton’s (2018) case study approach, I investigate the feasibility of implementing a multilingual pedagogy in an international school in Toronto and explore its influence on students, teachers, and the learning process across the domains of (CMLA) (Prasad & Lory, 2020). For this paper, I focus on data that highlight and reflect the impact of multilingual pedagogy on students, teachers, and the teaching/learning process. I performed a qualitative thematic analysis and found that multilingual pedagogies benefited students on many levels. I conclude with a personal reflection on both the affordances and challenges of implementing multilingual pedagogies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".