Locus equations as proxies for co-articulation lend support to the Degree of Articulatory Constraints model
Bibliographic record
Abstract
The degree of articulatory constraints (DAC) model (Recasens, Pallares, & Fontdevila, 1997) proposes that consonants involving the movement of the tongue dorsum are more resistant to coarticulation than those consonants that have a more fronted articulation. The present study aims to assess this claim using locus equation (LE) slopes as indicators of coarticulation. Participants were asked to produce V 1(t) .C 1 V 2 sequences as part of two-word phrases in a scripted dialogue, where C 1 is one of /p, t, s, ?/. LE were derived by measuring F2 at V 2 onset and midpoint. Since LE slopes approaching 1 indicate high levels of coarticulation, it was hypothesized that those segments with the lowest DAC would have the steepest slopes, and vice versa. /p/ was predicted to have the lowest DAC and steepest slope, followed by /t/, /s/, and /?/. Results were highly consistent with these hypotheses, lending support to the DAC model. A secondary hypothesis assessed the effect of emphatically stressing C 1 on the LE. Participants partook in a dialogue involving a “mishearing”, which prompted them to repeat the original V 1(t) .C 1 V 2 sequence. We expected participants to emphasize the misheard segment, which was either the target C 1 (Prominent condition) or the preceding V 1(t) (Control condition). It was predicted that prominence would reduce coarticulation, resulting in a downward shift in LE slopes relative to the Control condition. Our findings indicate that only the LE slopes of sibilants /s/ and /?/ were reduced under prominence as hypothesized, and that these reductions were statistically comparable. Results are thus consistent with the DAC model, since /s/ and /?/’s being less likely to co-articulate than /p/ and /t/ in the Prominent condition may due to their relatively large DAC values.
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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 | 0.004 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".