Clause Typing and Feature Inheritance of Discourse Features
Bibliographic record
Abstract
Abstract The aim of this article is twofold. First, we claim that δ‐features (discourse features), as well as ϕ‐features, can be inherited from C to T (Richards 2007, Chomsky 2008), as shown bywh‐agreement on T in Ojibwe (Algonquian). Our analysis supports Miyagawa's (2010) hypothesis that discourse and agreement features are two sides of the same coin, which can be distributed differently crosslinguistically. Second, we propose that although ϕ and δ typically bundle together on a single C head, this is not the case in all languages and in fact will vary parametrically. Ojibwe clause typing is partitioned between agreement/ϕ‐features on independent order (i.e., plain matrix) C and discourse/δ‐features on conjunct order (e.g., embedded) C. This parameter, that certain features may or may not bundle on C, captures a significant cluster of properties in Ojibwe: Initial Change, lack of person prefixes in the conjunct order in contrast with the independent, as well as the availability of long‐distance agreement. Our proposal supports the idea that much crosslinguistic variation reduces to the distinct feature structures making up functional heads, such as v,D, and C, rather than to primitives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".