The Handbook of Contemporary Syntactic Theory
Bibliographic record
Abstract
Contributors. Introduction. Part I: Derivation Versus Representation:. 1. Explaining Morphosyntactic Competition: Joan Bresnan (Stanford University). 2. Economy Conditions in Syntax: Chris Collins (Cornell University). 3. Derivation and Representation in Modern Transformational Syntax: Howard Lasnik (University of Connecticut). 4. Relativized Minimality Effects: Luigi Rizzi (Universite de Geneve). Part II: Movement:. 5. Head Movement: Ian Roberts (University of Stuttgart). 6. Object Shift and Scrambling: Hoskuldur Thrainsson (University of Iceland). 7. Wh--in--situ Languages: Akira Watanabe (University of Tokyo). 8. A--Movements: Mark Baltin (New York University). Part III: Argument Structure and Phrase Structure:. 9. Thematic Relations in Syntax: Jeffrey S. Gruber (independent scholar). 10. Predication: John Bowers (Cornell University). 11. Case: Hiroyuki Ura. 12. Phrase Structure: Naoki Fukui (University of California). 13. The Natures of Nonconfigurationality: Mark C. Baker (McGill University). 14. What VP Ellipsis Can Do, and What it Can't, but not Why: Kyle Johnson (University of Massachusetts at Amherst). Part IV: Functional Projections:. 15. Agreement Projections: Adriana Belletti (Universita di Siena). 16. Sentential Negation: Raffaella Zanuttini (Georgetown University). 17. The DP Hypothesis: Identifying Clausal Properties in the Nominal Domain: Judy B. Bernstein (Syracuse University). 18. The Structure of DPs: Some Principles, Parameters and Problems: Giuseppe Longobardi (University of Trieste). Part V: Interface With Interpretation:. 19. The Syntax of Scope: Anna Szabolcsi (New York University). 20. Deconstructing Binding: Eric Reuland and Martin Everaert (both Utrecht Institute of Linguistics). 21. Syntactic Reconstruction Effects: Andrew Barss (University of Arizona). Part VI: External Evaluation of Syntax:. 22. Syntactic Change: Anthony S. Kroch (University of Pennsylvania). 23. Setting Syntactic Parameters: Janet Dean Fodor (City University of New York). Bibliography. Index.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.043 |
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".