At the Intersection of Research & Pedagogy: Digital ‘Me Mapping’ with Newcomer Youth and Their Future Teachers
Bibliographic record
Abstract
Me-Maps are multimodal artifacts created by newcomer children and youth to document their linguistic profile, important milestones in their lives, their multiple identities, their experiences at school, and aspirations. Using a series of prompts and the Flipgrid application, the research team collaborated with teachers and NGO staff in creating Me-Maps. Starting in 2019, we integrated these Me-Maps into the Supporting English Learners course as a focus for teacher-candidate learning within the Master of Teaching. Engaging candidates with Me-Maps is based on Keet et al.’s (2009) notion of mutual vulnerability: teacher candidates open themselves up in the same ways newcomer students did, to create their own Me-Maps while also engaging with the digital me maps of students so as to learn from newcomers as complete humans, not simply as “language learners.” In this panel, we will 1) describe the Me-Mapping workshop process, 2) present an analysis of the content of the Me Maps created by newcomer students and their future teachers, 3) describe the perspectives of those who created their own Me-Maps and those who facilitated their creation, and 4) explore the implications for research, mainstream classrooms and teacher education as Me-Mapping straddles the realm of research and pedagogy.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".