It's Time to Recognize Canadian ELL Identities: Valuing Growth and Identity Formation Through First Language Use
Bibliographic record
Abstract
The aim of this qualitative research study was to gather teacher insights pertaining to the outcomes and feasibility of conducting Identity Texts with the English language learners in their classrooms. The main question that guided my research was: What are teachers’ perspectives on the feasibility and outcomes of multi-literacy projects (i.e. Identity Texts)? Data was collected through semi-structured interviews with three educators who currently work in TDSB schools. Findings suggest that conducting Identity Texts with ELLs has positive outcomes such as identity affirmation, literacy engagement, sharing and building relationships that promote more equitable spaces. In addition, the use of first languages was identified as a significant attribute of what makes Identity Texts successful. Findings also suggest that ELL students and ESL instructors both encounter challenges due to instances of marginalization, deficit thinking and stigma. The implications of these findings suggest that teachers need to be aware of the unique challenges and issues facing ELLs in schools and classrooms. Further recommendations include mandatory courses on ELL instruction in teacher programs so that teachers are better equip to incorporate these strategies and create more equitable spaces.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".