Position and place effects in Russian word-initial and word-medial stop clusters
Bibliographic record
Abstract
Studies of inter-gestural timing have shown that (i) word-initial obstruent clusters tend to exhibit less gestural overlap than word-medial or word-boundary clusters, and (ii) the degree of overlap is further affected by the place of articulation of the obstruents (Byrd, 1996; Chitoran, Goldstein, and Byrd, 2002). Both findings have been attributed to perceptual recoverability considerations. This paper presents results of a magnetic articulometer (EMMA) study of Russian word-initial and word-medial stop clusters (e.g., [pt]ashka little bird versus la[pt]a bat). Data collected from 3 native speakers of Russian show that clusters with coronals and dorsals as C1 ([tk], [kt], [kp], [tjm]/[djb]) exhibit less overlap word-initially than word-medially, while the cluster with the labial as C1 ([pt]) does not exhibit the same timing pattern. The findings are interpreted as providing additional support for the role of perceptual recoverability in intergestural timing. First, less overlap in word-initial clusters, compared to word-medial clusters, ensures better place recoverability of C1 (cf. Chitoran et al., 2002). Second, unreleased labials are more perceptually robust than unreleased coronals (Byrd, 1992; Surprenant and Goldstein 1998) and dorsals (Wright, 2001; Kochetov and So, 2005), and thus do not require the same degree of overlap. [Work supported by SSHRC.]
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".