Bibliographic record
Abstract
Abstract Many languages with ergative systems of case or agreement exhibitsplitsin their alignment. Viewpoint aspect is a common basis for such splits, with perfective aspect often associated with ergative alignment and imperfective with the absence of ergativity (Moravcsik , Silverstein ). Recent work has argued that splits arise from properties of the imperfective that disrupt otherwise‐available mechanisms of ergative alignment (Laka , Coon ). This article argues rather that the perfective can be asourceof ergative case, and specifically that ergative alignment in Hindi‐Urdu arises from the intersection of two different ways of expressing perfective aspect, each attested independently in other languages: the first is the use of oblique case to mark perfect or perfective subjects, while the second is a morphosyntactic sensitivity to transitivity, a hallmark of auxiliary selection in Germanic and Romance languages. The result is a more unified view of the morphosyntax of perfective aspect, though at the cost of a nonunified account of aspectually split 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 | 0.002 | 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 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".