An Interview with APPLE Lecture Speaker Professor Roy Lyster
Bibliographic record
Abstract
On February 16, 2018, Working Papers in Applied Linguistics and TESOL (represented by Kaylee Fernandez, Michelle Stabler-Havener, and Carol HoiYee Lo) had the great pleasure of interviewing Dr. Roy Lyster, the invited speaker for the 2018 Applied Linguistics & Language Education (APPLE) Lecture Series hosted annually by the Applied Linguistics and TESOL Program at Teachers College, Columbia University. Dr. Lyster shared his research and advice he has for current and future researchers and educators in Applied Linguistics and TESOL. Dr. Roy Lyster is Emeritus Professor of Second Language Education in the Department of Integrated Studies Education at McGill University in Montreal, Canada. His research examines content-based language teaching and the effects of instructional interventions designed to counterbalance form-focused and content-based approaches. His research interests also include professional development and collaboration among teachers for the purpose of integrated language learning and biliteracy development. He was co-recipient with colleague Leila Ranta of the 1998 Paul Pimsleur Award for Research in Foreign Language Education and was presented the Robert Roy Award by the Canadian Association of Second Language Teachers in 2017. He was co-president then president of the Canadian Association of Applied Linguistics from 2004 to 2008. He is author of a module called Content-Based Language Teaching published by Routledge in 2018, and two books: Learning and Teaching Languages Through Content published by Benjamins in 2007 and Vers une approche intégrée en immersion published by Les Éditions CEC in 2016.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".