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 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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".