Bibliographic record
Abstract
Studies of speech production have often focused on transient events—those that happen over short temporal intervals. We know, however, that speech is made up of movements that can be distributed over longer durations as well (e.g., tongue bracing, oralization, articulatory setting, laryngeal state, harmony, etc.). Such events, which involve maintaining continuous activation of a particular muscle group tonically over a long duration, have often been treated as qualitatively different from transient speech events. The present study considers examples of these types of movements in speech and non-speech (e.g., emotion expression, posture, etc.) domains. Biomechanical simulations are used to show how tonic activations operate on the same principles as transient ones (except for the difference in duration), and to show how tonic activations can overlap with multiple other activations—whether transient or tonic—through superposition [Bizzi et al. Science 253, 5017 (1991)]. This work aims to show how these previously anomalous kinds of movements fit seamlessly into broader theories of movement and speech. [Funding from NIH Grant DC-002717 and NSERC RGPIN-2015-05099.]
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".