Argument Structure and Antipassivization in Inuit
Bibliographic record
Abstract
This paper examines some seemingly disparate uses of the morpheme -si in two very closely related Inuit (Eskimo) languages: Inuktitut (spoken in Northeastern Canada) and West Greendlandic. I will use the term "Inuit" to refer to both West Greenlandic and Inuktitut as they pattern together. The morpheme -si is traditionally associated with antipassive morphology, although it also appears in several other environments that do not, at first, seem related to antipassivization. I will show that there are actually systematic patterns underlying the distribution of -si, indicating that the uses of of the morpheme in question are not as disparate as initial observations have assumed. I will discuss the implications of these patterns for a theory of the morphosyntax of antipassivization. Antipassivization is canonically a valency-changing operation that intransitivizes a transitive verb by "demoting" the direct object to an oblique Case or omitting it altogether. In Inuit, the antipassive is marked by a suffix which has several allomorphs. Many researchers claim that the specific allomorph which appears is lexically governed to some extent, and is at least partially abitrary or idiosyncratic, and not necessarily conditioned by phonological or other factors. Johnson (1980) and Bittner (1987) argue that the different antipassive morphemes are not allomorphs, but actually are used in different discourse contexts. The most common overt antipassive marker is -si, the one that we will focus on in this paper.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".