Bibliographic record
Abstract
This paper presents a detailed description and formal semantic analysis of habitual sentences in Tlingit (Na-Dene; Alaska, British Columbia, Yukon). As in many other languages (Carlson 2005, 2012), there are two means in Tlingit for expressing a habitual statement, such as my father eats salmon. The first employs a relatively unmarked verb, realizing imperfective aspect. In the second type of habitual sentence, however, the verb bears special habitual morphology. Although there is a significant overlap in the use of these constructions, certain semantic contrasts do exist. Most notably, the special habitual marking cannot be used to express pure, unrealized dispositions/functions/duties (e.g., Mary handles any mail from Antarctica). In other words, Tlingit habitual morphology --- unlike imperfective aspect --- requires the habituality in question to have actually occurred, an effect that has also observed for habitual morphology in a variety of other, unrelated languages (Green 2000, Bittner 2008, Boneh & Doron 2008, Filip 2018). I develop and defend a formal semantic analysis that captures these (and other) contrasts between imperfective and habitual verbs. In brief, imperfective aspect is argued to possess a modal semantics, quantifying over alternative worlds/situations (Arregui et al. 2014, Ferreira 2016). Habitual morphology, however, is argued to be associated with a (potentially covert) quantificational adverb, one that quantifies strictly over times in the actual world. The consequences of this account for the analysis of habitual sentences in other languages are explored. Most notably, we find that (i) “habituality” so-called is potentially a heterogeneous phenomenon, and resists unified definition or semantic analysis, and (ii) therefore is a sui generis category of phenomena, which cannot be reduced as an instance of aspect or modality (Filip & Carlson 1997, Filip 2018). EARLY ACCESS
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".