Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it