Continuum companion to research methods in applied linguistics
Bibliographic record
Abstract
List of contributors 1. Introduction, Brian Paltridge and Aek Phakiti (University of Sydney, Australia) Part I: Research methods and approaches 2. Experimental research, Susan Gass (Michigan State University, USA) 3. Survey research, Elvis Wagner (Columbia Teachers' College, USA) 4. Analysing quantitative data, Aek Phakiti (University of Sydney, Australia) 5. Ethnographies, Sue Starfield (University of New South Wales, Australia) 6. Case studies, Christine Pearson Casanave (Temple University, Tokyo) 7. Action research, Anne Burns (Macquarie University, Australia) 8. Analysing qualitative data, Adrian Holliday (Christ Church University, UK) 9. Research syntheses, Lourdes Ortega (University of Hawa'ii, USA) 10. Critical research in applied linguistics, Steven Talmy (University of British Columbia, Canada) Part II: Areas of research 11. Researching speaking, Rebecca Hughes (University of Nottingham, UK) 12. Researching listening, Larry Vandergrift (University of Ottawa, Canada) 13. Researching reading, Marie Stevenson (University of Sydney, Australia) 14. Researching writing, Ken Hyland (University of London, UK) 15. Researching grammar, Neomy Storch (University of Melbourne, Australia) 16. Researching vocabulary, David Hirsh (University of Sydney, Australia) 17. Researching pragmatics, Carsten Roever (University of Melbourne, Australia) 18. Researching discourse, Brian Paltridge and Wei Wang (University of Sydney, Australia) 19. Researching language classrooms, Lesley Harbon and Huizhong Shen (University of Sydney, Australia) 20. Researching language testing, John Read (University of Auckland, New Zealand) 21. Researching motivation, Lindy Woodrow (University of Sydney, Australia) 22. Researching language and gender, Jane Sunderland (Lancaster University, UK) 23. Researching language and identity, David Block (University of London, UK) Glossary of key research terms Index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.225 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.422 | 0.389 |
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".