Predicting stress patterns in an unpredictable stress language: The use of non-lexical sources of evidence for stress assignment in Russian
Bibliographic record
Abstract
The main goal of this research was to examine how readers of Russian assign stress to disyllabic words. In particular, we tested the claim that the process of stress assignment in Russian can only be accomplished lexically. Eleven potential non-lexical sources of evidence for stress in Russian were examined in regression and factorial studies. In Study 1, onset complexity, coda complexity, the orthography of the first syllable (CVC1), of the second syllable (CVC2), and of the ending of the second syllable (VC2) were found to be probabilistically associated with stress in Russian disyllables. In Studies 2 and 3, it was shown that Russian speakers do use 3 of these cues (CVC1, CVC2, and VC2) when making stress-assignment decisions. These results provide evidence against the idea that the nature of stress in the Russian language is so unpredictable that stress assignment can only be accomplished lexically. These results also suggest that any successful model of stress assignment in Russian needs to contain mechanisms allowing these 3 orthographic cues to play a role in the stress-assignment process.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".