Bibliographic record
Abstract
Abstract This article surveys empirical and theoretical work on Tense‐Aspect‐Mood (‘‘TAM’’) based split ergativity, and offers an account for how it arises. While these splits are typically assumed to represent a unified phenomenon, I demonstrate that non‐ergative portions of split systems exhibit different patterns. I argue that these patterns reflect at least two different triggers of split ergativity: (i) non‐perfective aspects are more likely to be built on complex auxiliary constructions, and (ii) imperfectivity is associated with demoted objects or lower transitivity. Both causes trigger the same result: in the ‘‘split’’ portions of the grammar the transitive subject is not marked with ergative case because it is not a transitive subject. This structural account of split ergativity allows us to avoid positing variable feature inventories on the same functional head (cf. ), and also provides a straight‐forward account of the so‐called ‘‘counter‐universal’’ splits (), which cause problems for purely functionalist accounts (e.g. ). Furthermore, it is shown that the factors which trigger these splits are not limited to ergative languages, but are present cross‐linguistically—they are not visible in nominative‐accusative systems because (by definition) there is no visible difference between transitive and intransitive subjects. The prevalence of splits in ergative systems is thus not taken to reflect any deep instability of ergativity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".