Bibliographic record
Abstract
The use of high-rise terminals, or uptalk, continues to be a point of contention in the study of intonation. Researchers continually produce conflicting descriptions of it in an effort to define it, including conflicting explanations of its connotation or significance in conversation. These difficulties suggest that ToBI, the most frequently used system of annotation for intonation, cannot adequately describe the contour. The definitions of uptalk produced using ToBI do not allow it to be distinguished from other contours, namely question intonation. This study tries to define the contour, not by describing it as ToBI does, but by measuring the pitch excursions which speakers produce in these contours in relation to the overall pitch range of the phrase in which it was produced. The results of the study show that the excursions produced in uptalk fall in the mid range of rises, steeper than those of other declarative statements, but not as steep as those produced in question intonation. This provides a potential set of criteria which can be used to precisely distinguish uptalk contours. The ability to identify contours based on the height of the pitch excursion exhibited also points to the possibility of the use of categorical perception of intonation contours by native speakers of English.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".