Bibliographic record
Abstract
This paper presents an overview on deverbal nominalizations from Ktunaxa, a language isolate spoken in eastern British Columbia, Canada. Deverbal nominalizations are formed uniformly with a left-peripheral nominalizing particle k (Morgan 1991). However, they do not form a single homogenous class with respect to various syntactic properties. These properties are illustrated with novel data, showing that deverbal nominalizations fall into at least two classes, which are analyzed here as nominalization taking place at either vP or VP, where vP-nominalizations include the external argument and VP-nominalizations do not. Evidence for this division comes from how possession is expressed, the interpretation of the passive (and passive-like constructions), and the licensing of verbal modifiers. As both classes of deverbal nominalizations are constructed uniformly with the nominalizing particle, these properties are derived syntactically from the size of the verbal constituent being nominalized. Dieser Beitrag ist ursprünglich im Peter-Lang-Verlag erschienen (https://www.ingentaconnect.com/contentone/plg/jwf/2020/00000004/00000002/art00004)
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".