Case-Dependent Agreement in an Active–Stative Language
Bibliographic record
Abstract
This paper revisits the cross-reference marking system of Mbyá Guaraní, focusing on two phenomena: object agreement using the prefix i- and its allomorphs, and absolutive cross-reference marking in converbs. The analysis demonstrates that cross-reference marking in Mbyá is sensitive to abstract Case. Building on a view of agreement as an obligatory operation whose failure does not result in ungrammaticality, this paper argues that the segment i- is an object agreement prefix, rather than part of an allomorph of an active subject agreement prefix. This marker is underspecified for person, allowing it to cross-reference 1st, 2nd or 3rd objects. The paper further argues that converbs in Mbyá Guaraní follow an absolutive cross-reference marking pattern, where only intransitive subjects or objects are cross-referenced. This pattern is shown to be consistent with cross-linguistic and historical data from the Tupí–Guaraní family. This paper’s contributions include a proposal for case-sensitive agreement in Mbyá, with active agreement prefixes realizing agreement with nominative DPs only. The analysis also emphasizes the different roles of Infl and little v as probes for person features, with little v being underspecified and not triggering cyclic expansion. The proposed framework accounts for both hierarchical cross-reference marking in independent clauses and absolutive marking in converbs, unifying these two patterns under the assumption of Case dependence of agreement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".