Bibliographic record
Abstract
Hupa (Na:tinixwe Mixine:whe’) is a Pacific Coast Dene language spoken in Hoopa Valley in Northern California. Like its Dene sisters, Hupa exhibits complex verbal morphology which has attracted decades of theoretical research. One approach that has yet to have been applied to these languages is information theory. Previous information-theoretic research into verbal morphology has uncovered a cross-linguistic trend of grouping predictive information closer together and finding morphemes that are more mutually-informative to the root closer to the root, which in turn reduces overall surprisal and is easier on memory constraints. However, these studies analyzed prominently suffixing languages of Afro-Eurasia. This project is the first application of these information-theoretic concepts to a Dene language to investigate if these approaches also apply to explain morpheme order in a low-resource, Indigenous American language with intricate, prominently-prefixing morphology. The results indicate similar findings to previous research. Hupa demonstrates a word-level linear morpheme order that, on average, orders most mutually-informative morphemes closest to the verb root compared to a randomized baseline. This morpheme order also resulted in an average surprisal that was more comparable to optimized morpheme orders than a randomized baseline. Morpheme-type mutual information, however, demonstrates the discrepancies between word- and templatic-level information content in Hupa, which exemplifies the word-level efficiency that Hupa shares with other languages despite the typological uniqueness of its morphological grammar.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".