Bibliographic record
Abstract
Synopsis: This book focuses on the role size plays in grammar. Under the umbrella term size fall the size of syntactic projections, the size of feature content, and the size of reference sets. This Volume II discusses size effects in movement, agreement, and interpretation while the contributions in Volume I focus on size and structure building. Part I of Volume II investigates how size interacts with head movement and various phrasal movement including left branch extraction, object shift, tough movement, and multiple wh movement. Part II of this volume discusses the role size plays in agreement and morphology-related matters like allomorphy. Contributions in Part III focus on semantic-oriented issues, in particular the size of reference domains and NPI licensing. The languages covered in this volume include American Sign Language, Bosnian-Croatian-Serbian and various other Slavic languages, German, Icelandic, dialects of Italian, Japanese, Nancowry, Panoan languages, and Tamil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".