Bibliographic record
Abstract
Abstract. This article refutes the claim ( Swift and Bohnemeyer 2004 ) that viewpoint aspect in Inuktitut is determined wholly by telicity. We claim that, in fact, Inuktitut lacks default viewpoint aspect altogether. This article follows Cowper (2005) and Kyriakaki (2006) in assuming that viewpoint aspect can be encoded with the morphosyntactic features Interval (imperfective aspect) and Moment (perfective aspect), which are possible dependents of the feature Event. We show that, while English has two past tense constructions, one of which is infelicitous with states because it spells out Interval, the dependent feature of Event, Inuktitut has only one. There is no tense construction in Inuktitut that is felicitous only with events, indicating that there is none that spells out a dependent of the feature Event. Other tests for perfectivity and imperfectivity prove inconclusive in Inuktitut; there is no evidence of a feature Interval or Moment. Thus, we conclude that no such feature exists. Therefore, all Inuktitut clauses are neither perfective nor imperfective, but simply unmarked with respect to viewpoint aspect. We further show that the locus of all aspectual variation in Inuktitut is little v, and that this variation relates to lexical aspect only, not viewpoint aspect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".