Bibliographic record
Abstract
Foreword Brian Joseph (Ohio State University) 1. Introduction Koenraad Kuiper 2. A Toolbox for Teaching Phonetics Jen Hay (University of Canterbury, New Zealand) 3. Learning Phonology as a Way to Learn how Theories are Improved Andrew Carstairs-McCarthy (University of Canterbury, New Zealand) 4. Teaching Morphology Laurie Bauer (Victoria University of Wellington, New Zealand) 5. Teaching Syntax Sandra Chung (University of California, Santa Cruz) 6. Teaching Formal Semantics Barbara Partee (University of Massachusetts at Amherst) 7. Teaching Pragmatics Chris Potts (Stanford University) 8. Teaching Historical Linguistics: A Personal Memoir Harold Koch (Australian National University) 9. Teaching Sociolinguistics Miriam Meyerhoff (University of Auckland) 10. Psycholinguistics for Linguists Paul Warren (Victoria University of Wellington, New Zealand) 11. Teaching Linguistic Approaches to Nonliteral Language or We Really Knew how to have Fun Diana van Lanker-Sidtis (New York University) 12. Developmental Psycholinguistics Susan Foster-Cohen (Burwood Hospital, New Zealand) 13. The Value of Linguistis to the ESL/EFL Classroom Practitioner David Mendelsohn (York University, Canada) 14. Games for Exploring Language Origins and Change Alison Wray (Cardiff University) 15. LING 101 Koenraad Kuiper 16. 'Beyond Compare': Supervising Postgraduate Research Janet Holmes (Victoria University of Wellington, New Zealand) 17. Field Methods: Where the Rubber Meets the Road Wes Collins (Summer Institute of Linguistics) 18. 'Two Loaves where there Seems to be One' : Metaphors We Teach By Kate Burridge (Monash University)
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".