Bibliographic record
Abstract
This thesis presents a study of final vowel devoicing in Blackfoot, an indigenous language of Montana and Alberta. Previous research on final vowel devoicing in Blackfoot variously suggests word-final, phrase-final, and utterance-final vowel devoicing processes (e.g. Taylor 1965, Bliss & Gick 2009, Frantz 2017), though, the conditioning environment for this phenomenon had not been a research focus prior to this study. The present study investigates intonation units (IUs) as the conditioning domain for final vowel devoicing in Blackfoot. Final vowel devoicing in Blackfoot is investigated here by examining the common word-final suffixes –wa (3SG.AN) and –yi (4SG) in two recordings of connected speech. Each recording features a different native speaker of Blackfoot. Speakers were asked to generate a narrative to go along with illustrations in a picture book. These recordings are interlinearized using ELAN annotation software. Next, tokens of –wa and –yi are analyzed acoustically using Praat phonetic software. Then, –wa and –yi tokens are analyzed in terms of their position within the intonation unit (IU-medial or IU-final). Finally, the data are collated, giving the frequencies of different phonetic variants as well as the distribution of phonetic variants across IU-medial and IU-final environments. The findings of this study are that fully-audible variants of –wa and –yi almost always occur IU-medially, while devoiced variants are most frequently found in IU-final position. Based on these findings, this thesis proposes an IU-final vowel devoicing rule to describe the phonetic variation and distribution of –wa and –yi in connected speech. The analysis put forth in this thesis has implications for the theoretical classification of vowel devoicing phenomena, for linguistic research methodologies, and for the typology of intonation units cross-linguistically. Furthermore, the findings of this work bear on language documentation, revitalization, and pedagogy.
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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 | 0.001 | 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) | 0.004 | 0.006 |
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".