Bibliographic record
Abstract
Abstract Christopher N. Candlin (1940–2015) was a major figure in applied linguistics, particularly in the areas of communicative language teaching and professional communication. He had a BA in modern languages from Oxford, an MPhil in linguistics from Yale, and an honorary PhD from the University of Jyväskylä, Finland, and has held teaching and research posts at the University of Leeds, University of Lancaster, Macquarie University, City University of Hong Kong, and the Open University (UK). He has also held visiting and honorary professorships at a number of institutions including the University of Hawai'i, the East‐West Center, Ontario Institute for Studies in Education, and University of Cardiff. Concurrent with these posts he established a number of important research institutes and centers including the Institute for English Language Education at the University of Lancaster in 1976, the Centre for Language in Social Life at the University of Lancaster in 1983, the National Centre for English Language Teaching and Research (NCELTR) at Macquarie University in 1987, which was a Commonwealth Key (National) Centre for Teaching & Research, and the Centre for Language in Social Life at Macquarie University, Sydney in 1994. He served as head of the department of linguistics and modern English language at the University of Lancaster between 1980 and 1983. At the time of his passing, he held the title of Senior Research Professor Emeritus at Macquarie University. He has also been an important force in publishing in applied linguistics, editing 11 international book series with such publishers as Oxford University Press, Addison Wesley Longman, Routledge, and Palgrave Macmillan.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.026 |
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".