Lateral bias in lingual bracing during speech
Bibliographic record
Abstract
Human bodies exhibit lateral biases between many laterally symmetrical body parts (e.g., hands, feet, eyes, and ears) that increase the efficiency of behaviour and functionality of a system. We report on lateral biases observed in the tongue during speech. The tongue is bilaterally braced against the back teeth and hard palate throughout speech, but is interrupted for the production of some laterals and occasional low vowels; some evidence suggests the movement away from the braced posture may be produced by lowering one side of the tongue first and that the leading side is consistent within speaker [Gick et al. 2017, JSLHR 60(3), 494]. We report findings on lateral bias in English speakers, on its correlation with other lateral biases of the speakers, and what this may imply about the origins of this bias. Preliminary results indicate some variation, with a population-level bias (preference for one side over the other), suggesting that the bias may develop with cortical modulation in much the same way that handedness is thought to arise. [Funding through NSERC.]
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".