Pausing as a prosodic correlate of speech units in St’át’imcets (Lillooet Salish)
Bibliographic record
Abstract
The reliability of pausing as a correlate to prosodic, syntactic and discourse units has been debated in commonly-studied languages (outlined, for instance, in Krivokapic 2007). Preliminary research in Nxaʔamxčín (Interior Salish) (Caldecott & Czaykowska-Higgins 2012) has shown that pausing could be a more reliable acoustic correlate of these structures than pitch, in line with previous research indicating that Salish languages do not exhibit a strong reliance on pitch to mark information structure (Caldecott 2017; Caldecott & Czaykowska-Higgins 2012; Davis 2012; Koch 2008, 2011). The current study further tests this hypothesis by examining the occurrence and duration of pauses in a 9.5-minute spontaneous narrative by a fluent speaker of St’át’imcets (Interior Salish). Pause distribution with respect to syntactic and prosodic boundaries is analysed. Preliminary analysis suggests that St’át’imcets follows previous research on spontaneous narratives in some respects but not others. Very long pauses (<5.5 s) mark major thematic shifts (Oliveira 2000) and most pauses (70%) occur clause-finally (Henderson, Goldman-Eisler & Skarbek 1966). Most clause-medial pauses occurred between a determiner and noun, following Goldman-Eisler (1968), but contra Gee & Grosjean (1983). A t-test indicates that clause-final vs. clause medial pauses are not significantly different in duration (p>.05) (contra Goldman-Eisler 1972).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".