Stem Alternation in Tłıchǫ Yatıì Classificatory Verbs: A Cognitive Semantic Account
Bibliographic record
Abstract
This paper investigates the phenomenon of ‘classificatory verbs,’ i.e., a set of motion and positional verbs that show stem alternation depending on the semantic features of one of their arguments. The data is drawn mainly from Tłı̨chǫ Yatıì Multimedia Dictionary, Nicholas Welch’s field notes, and other documentary sources of the language. Tłı̨chǫ classificatory verbs are presented and analyzed in detail. The paper argues that Tłı̨chǫ Yatıì classificatory verbs belong to four semantic subclasses and that these subclasses show a decreasing degree of stem alternations related to argument classification. The inconsistency in stem alternation is triggered by the presence or absence of some semantic features that determine the number of stem allomorphs. Locative verbs are affected by the [COMFORT] feature, and the other three sets are influenced by [TRANSFER], [INITIAL AGENTIVE] and [FINAL AGENTIVE] features. Moreover, the paper outlines a semantic feature geometry that accounts for the observed regularities in classificatory verb stems and their possible variations intra- and cross-linguistically.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".