Ergativity: Argument Structure and Grammatical Relations. CHRISTOPHER D.
Bibliographic record
Abstract
dissertation. As a syntactician who is not an expert on ergativity I enjoyed the book, finding it clearly written and carefully argued. While a large amount of data is included to back up the empirical claims made, it is carefully presented not to overwhelm the reader. Also, while one of the central thrusts is a theoretical one, the claims are made in as theory-neutral a way as possible, making this book useful to linguists of various stripes. Part 1, Cutting the Ergative Pie, outlines the core claims of the book, backed up by various theoretical and empirical cross-linguistic considerations; Part 2, Inuit (West Greenlandic), is an in-depth look at Inuit, a well-studied ergative language; there Manning compares his account of ergativity to others from the literature. The basic claim is that a syntactic representation is organized into two levels of information: grammatical relations structure (gr-structure) and argument structure (astructure) and that one locus of variation among languages is in the linking between the two levels of representation. Gr-structure corresponds roughly to a surface level of grammatical relations, like the final grammatical relations of Relational Grammar (RG), the level of f-structure in Lexical-Functional Grammar (LFG), or the level of S-structure
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".