Bibliographic record
Abstract
Abstract Originally conceived of as a large‐scale macroparameter, non‐configurationality was thought to delimit languages lacking in hierarchical phrase structure (Chomsky 1981). However, non‐configurationality can be defined instead as a cover term for languages in which there is hierarchical phrase structure, but the evidence for such structure is obscured. In the narrow sense, non‐configurational languages are those that meet three criteria: (i) free word order, (ii) extensive null anaphora, and (iii) discontinuous expressions (Hale 1983). In the broad sense, non‐configurational languages are those that fail a variety of tests for asymmetric c‐command between the subject and the object, and/or tests for VP constituency. These languages are genetically, geographically, and typologically diverse, and they cannot be classified as a single language type. There have been various attempts to reduce non‐configurational properties to a single grammatical source or macroparameter: the Dual Structure approach (e.g., Austin and Bresnan 1996), the Pronominal Argument approach (e.g., Baker 1996), and the Computational Relevancy approach (Pensalfini 2004) All of these approaches focus on the paradoxical nature of non‐configurational languages: they exhibit subject/object asymmetries in some domains, but not others. However, these approaches, which treat non‐configurationality as a macroparameter, are neither theoretically nor empirically motivated. Other work on non‐configurational languages such as Warlpiri, Mohawk, the Salish languages, and the Algonquian languages have paved the way for a microparametric view of non‐configurationality, in which various diverse and independently motivated grammatical principles can conspire to yield a non‐configurational profile.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".