Plurilingual teachers and their experiences navigating the academy
Bibliographic record
Abstract
Drawing on qualitative data collected from plurilingual teachers in the context of three research studies conducted at the University of Toronto between 2004 and 2015, this paper critically examines, through a dialogue between the three researchers, the experiences of plurilingual teacher candidates and graduate students in Education as they navigate the academy. A trioethnographic methodology is used, unpacking the underlying tensions of roles and positions held by each of the researchers in the Student Success Centre (SSC) which offers a range of support services and provides a space where plurilingual teacher learners can interact with plurilingual tutors during their academic journey which may include practica and internships. We relate our findings focussed on the SSC to the literature on diverse teachers in universities as well as writing centre research calling for significant changes in how to support plurilingual students in the academy in order to highlight lessons and strategies for equity.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".