Bibliographic record
Abstract
Abstract Word classes have been controversial in Salish language research, but the current consensus among Salishanists is that the concepts of noun and verb, as well as adjective and adverb, are important for constructing insightful grammars of the languages. Drawing on primary data from the Central Salish language Halkomelem as well as discussions of word classes by various scholars, we show that entity-denoting and event-denoting words can clearly be related to nouns and verbs respectively. Nouns and verbs exhibit clear inflectional differences even though some inflectional processes—such as tense, plural, and diminutive marking—pertain to all lexical classes. We also illustrate morphological processes that change the lexical class of a word. Adjectives have been understudied in Salish languages, which have few true adjectives, as modification is also accomplished by means of stative-resultative forms. Adverbs divide into two types—those that must be followed by a linker and those that can occur in many positions. We conclude that categorial distinctions are relevant to the description and analysis of Salish languages at all levels: root, word, phrase, and clause.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".