Every Teacher is a Language Teacher: Social Justice and Equity through Language Education (Vol. 2)
Bibliographic record
Abstract
This volume is a result of our annual event, Every Teacher is a Language Teacher, held in 2021 and 2022 at the Faculty of Education of the University of Ottawa. Every year, we organize professional learning workshops for teacher candidates in our program. We invite seasoned teachers and young researchers (many of whom are also teachers!) doing their graduate work at the faculty to share methods, ideas, strategies and activities. Together, our community explores how to work with English and French language learners, as well as learners who come to the classroom with multiple languages in their repertoire. As research and experience have shown us, accessing students’ funds of linguistic and cultural knowledge is a powerful way to include students in the curriculum and center their contributions, identities, and experiences in the learning process. As a community of committed educators, we practice teacher learning with this same mindset – we draw from the personal and professional experiences of our guest presenters and teacher candidate attendees. Together, we speak to the social, cultural and political issues that matter in the classroom and share cutting-edge practices. The chapters in this book are intended for teachers who may or may not have a language teaching specialization. So as to promote equitable access to knowledge, we asked our presenters to write chapters based on their workshops and the experiences they had with our teacher candidates. They write in direct, accessible language and draw on the literature about language learning and teaching grounding their work in tried and tested initiatives. As such, for the reader, we hope the chapters will provide insight into what occurred during the workshops and how they can benefit from these professional learning sessions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".