Bibliographic record
Abstract
Ergative case is said to mark transitive subjects, and it is widely assumed that this is true under the ordinary definition of transitive; however, Bittner and Hale (1996) propose that ergative languages fall into two types, neither of which is based on the ordinary notion of transitivity. In one, a direct object is not necessary for ergative case: any verb with an external argument counts as transitive, following Hale and Keyser 1993 (e.g., Warlpiri). In the other, a direct object is necessary, but not sufficient: the subject gets ergative case only if the object moves out of the VP (e.g., Inuit). This article argues that Niuean, Dyirbal, and Nez Perce are also of this object shift type. A search yielded no language where ergative case is clearly governed only by ordinary transitivity; languages that do fit the stereotype have only ergative agreement. A formal account of the correlation between object shift and ergative case is proposed, under which ergative case can be used as a ‘‘last resort,’’ as one of three ways to avoid the locality violation that object shift creates.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".