LITTLE v IN INUKTITUT: ANTIPASSIVE REVISITED
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Bibliographic record
Abstract
This paper provides an in-depth examination of the distribution of the Antipassive morpheme -si-in Mittimatalik, a dialect of !nuktitut spoken in North Baffin.It is demonstrated that the occurrence of the overt Antipassive marker in Mittimatalik correlates with the argument structure of the verb.The analysis of the Antipassive takes into account the fact that only inherently transitive verbs and causativized verbs require the Antipassive marker in the Antipassive construction.It is proposed that the Antipassive morpheme -si-occupies v and is responsible for accusative assignment to the patient argument.Assuming that v is the focal point of transitivity, the differences in argument structure lie therefore in the feature make-up of v.The proposal accounts for the fact that the distribution of the Antipassive morpheme coincides with the argument structure of the verb.Moreover, the analysis is able to predict the occurrence of the Antipassive morpheme in Mittimatalik. INTRODUCTION-WHAT IS ARGUMENT STRUCTURE?Based on an analysis of the Antipassive in Inuktitut, this paper argues for a structural view of argument structure where at the same time showing argument structure (AS) to be a lexical property of the verb.The latter property is to be understood in the sense that the verb minimally merges with a lexically predetermined number of arguments in also predetermined structural configurations.In this sense, I take AS to be similar to Hale and Keyser's (1993) term I-syntax.I understand the term I-syntax as synonymous to a structural representation of a verb's AS.Any modification thereof, is taken to occur in syntax proper, or s-syntax in Hale and Keyser's terms.AS is therefore the least number of arguments a verbal head needs to merge with.If not indicated otherwise, all Inuktitut data are taken from my fieldwork with Ida Awa, a speaker of Mittimatalik.My thanks for her efforts to explain the semantic subtleties to me, which I hope I will understand eventually.I would also like to thank my supervisor Alana Johns for keeping me on my toes throughout the writing of this paper.Additional thanks go to Rebecca Smollett for proofreading
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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