Bibliographic record
Abstract
This study examines how speakers use acoustic cues, e.g., pitch and pausing, to establish syntactic and semantic constituents in Nehiyawewin, an Algonquian language. Two Nehiyawewin speakers autobiographies, which have been recorded, transcribed, and translated by H. C. Wolfart in collaboration with a native speaker of Nehiyawewin, provide natural-speech data for the study. Since it is difficult for a non-native-speaker to reliably distinguish Nehiyawewin constituents, an intermediary is needed. The transcription provides this intermediary through punctuation marks (commas, semi-colons, em-dashes, periods), which have been shown to consistently mark constituency structure [Nunberg, CSLI 1990]. The acoustic cues are thus mapped onto the punctuated constituents, and then similar constituents are compared to see what acoustic cues they share. Preliminarily, the clearest acoustic signal to a constituent boundary is a pitch drop preceding the boundary and/or a pitch reset on the syllable following the boundary. Further, constituent boundaries marked by a period consistently end on a low pitch, are followed by a pitch reset of 30–90 Hz and have an average pause of 1.9 seconds. I also discuss cross-speaker cues, and prosodic cues that do not correlate to punctuation, with implications for the transcriptional view of orthography [Marckwardt, Oxford 1942].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".