Bibliographic record
Abstract
Professor Jack C. Richards has been an enduring and highly influential contributor to the field of applied linguistics and English language teaching for the best part of 60 years. He has touched the professional lives of many people working in these fields all over the world – researchers and academic colleagues, materials writers, undergraduate, graduate and doctoral students, teacher educators and teachers, and language learners, and publishers. His publication output is legendary and it continues to guide and inspire the ELT profession. To provide an example of how Professor Richards influences, supports and mentors those he works with, in this paper I trace my own personal history and experiences of my encounters with his publications, and my collaborations with him as an author. My intention is to tease out, at least in part, the scope of his many professional interests and the way in which they have affected my own work. These works include a focus on the teaching of speaking and listening, second language teacher education, language teaching, pedagogy and curriculum development, and language learning. The account is necessarily and unavoidably selective, as it is derived from my personal experiences of working with him for the last quarter century. However, my overall intention is to pay tribute to his remarkable career.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.025 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".