Articulatory conflict resolution strategies among L1 and L2 SENCOTEN speakers
Bibliographic record
Abstract
This paper documents variation in the strategies used by SENCOTEN speakers of different generations and fluency levels to overcome articulatory conflicts, i.e., sequences of sounds that are difficult to pronounce because they require conflicting configurations of the articulators (Gick & Wilson, 2006). Previous work on SENCOTEN /qi/ and /iq/ sequences (Bird & Leonard, 2006; Bird, 2012) is based on two fluent L1 speakers, and shows that three main strategies are used: 1) compromise of the vowel (/iq/ > [Iq]); 2) insertion of a transitional element, often a fricative (/iq/ > [ixq]); and 3) dynamic tongue movement during the /q/ closure (/iq/ > [ikq]). Crucially, all of these strategies maintain some acoustic evidence of the uvular /q/ closure. One of the concerns among the SENCOTEN-speaking community is that the velar~uvular contrast is being lost in younger (L2) speakers (Bird & Kell, 2015). If this is the case, it is likely that articulatory conflict resolution strategies would reflect this, for example /iq/ > [ik]. To explore this possibility, a phonetic study was conducted on two specific sequences: /iq/ and /sq/; both of these require moving quickly between a high, advanced tongue position and a (relatively low) retracted position. Target words containing these sequences (e.g. /sqaxe7 ‘dog’; /st’iqel/ ‘bog’) were recorded by 12 speakers varying in generation and fluency level. Preliminary results show that (younger) L2 speakers do indeed tend to pronounce /q/ as [k] in these sequences, whereas their elders (L1 speakers) use a variety of strategies that, for the most part, maintain /q/. Findings offer insight into the strategies used to ease pronunciation among L2 learners, and also give us valuable direction in terms of teaching and learning SENCOTEN.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".