Bibliographic record
Abstract
A Word from the Editors It is our great pleasure to present to you the 2019 edition of the TESL Canada Journal.In this issue, you will fi nd fi ve articles that each draw a ention to diff erent dimensions of language teaching.In the fi rst article, Literacy Engagement in Multilingual and Multicultural Learning Spaces, Theodora Kapoyannis challenges monolingual and monocultural norms in literacy practices with young learners.In this study, the author describes a collaborative project in which young learners drew on culturally relevant texts to bolster their literacy development.The project proved to be valuable in developing vocabulary acquisition through the creation of identity texts that allowed students to use language in ways that were personally meaningful.The second article draws a ention to the professional lives of adult English language teachers.In this article, The Precarious Work of English Language Teaching in Canada, Sherry Breshears examines the nature and conditions of employment of English as a second language (ESL) teachers.By drawing on data that document the experiences of teachers, the author brings to the fore the ways in which multiple factors contribute to precarious employment.She calls for collective mobilization and action on the part of professional associations to address these issues.In the next article, Teaching in Linguistically and Culturally Diverse Classrooms in Canada: Self-Effi cacy Perceptions of Internationally Educated Teachers, the discussion shifts to a focus on Internationally Educated Teachers (IETs) and their self-effi cacy beliefs for teaching in linguistically and culturally diverse K-12 classrooms.In this study, researchers Mithila Vidwans and Farahnaz Faez found that IETs reported greater self-effi cacy, than non-IETs, in providing culturally responsive pedagogy in multilingual classrooms.Also situated in the K-12 context, Ana Vintan and Tiff any L. Gallagher explore how ESL teachers and elementary classroom teachers collaborated to support English language learners.In this article, Collaboration to Support ESL Education: Complexities of the Integrated Model, the authors examine multiple sources of data to understand how teachers draw on pedagogy and materials to work together and support learners.The fi ndings highlight specifi c barriers that proved challenging to ESL teachers and suggest how these eff orts can be be er supported.In the fi nal article, The Knowledge Base of L2 Pronunciation Teaching: The Case of a Nonnative-Speaking Teacher, Joshua Gordon draws our a ention to a specifi c pedagogical context-teaching second language pronunciation-and asks how nonnative-speaking teachers of English draw on their professional knowledge to make decisions about pedagogy.Through this case study, Gordon provides insight into the complexity of teacher professional knowl-
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.196 | 0.175 |
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".