Bibliographic record
Abstract
1. What is Word Grammar? Richard Hudson, (University College London, UK) I: Word Grammar Approaches to Linguistic Analysis 2. Case Agreement in Ancient Greek: Implications for a Theory of Covert Elements, Chet Creider (University of Western Ontario, Canada) and Dick Hudson 3. Understood Objects in Japanese and English: A Word Grammar Account, Kensei Sugayama (Kobe City University, Japan) 4. The Grammar of Be To: From a Word Grammar point of view, Kensei Sugayama (Kobe City University, Japan) 5. Linking in Word Grammar, Jasper Holmes 6. Word Grammar and Syntactic Code-Switching Research Eva Eppler (University of Surrey, Roehampton) 7. Word Grammar Surface Structures and HPSG Order Domains, Takahumi Maekawa (University of Essex, UK) II: Towards a Better Word Grammar 8. Distributional Heads and Structural Heads, And Rosta (University of Central Lancashire, UK) 9. Factoring out the Subject Dependency, Nik Gisborne (University of Edinburgh, Scotland) 10. Conclusion, Kensei Sugayama.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".