One hundred years of instrumental phonetic fieldwork on North America Indian languages
Bibliographic record
Abstract
A resurgence of interest in phonetic fieldwork on generally morphologically complex North American Indian languages over the last 15 years is a continuation of a tradition started a century ago with the Earle Pliny Goddard, who collected kymographic and palatographic field-data between 1906–1927 on several Athabaskan languages: Coastal Athabaskan (Hupa and Kato), Apachean (Mescalero, Jicarilla, White Mountain, San Juan Carlos Apache), and several Athabaskan languages in Northern Canada (Cold Lake and Beaver); data that remains important for its record of segmental timing profiles and rare articulatory documentation in then largely monolingual communities. This data in combination with new work has resulted in the emergence of a body of knowledge of these typologically distinct families that often challenge notions of phonetic universality and typology. Using the Athabaskan languages as benchmark example and starting with Goddard’s work, two types of emergent typological patterns will be discussed; the persistence of fine-grained timing and duration details across the widely dispersed family, and the broad variation in prosodic types that exists, both of which are unaccounted for by phonetic or phonological theories.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".