The lexical representation of word stress in Russian
Bibliographic record
Abstract
This paper explores the processing of metrical structure in Russian, a language with free lexical stress. According to the existing theoretical accounts, not all Russian stems are specified for accent in the lexicon. The present study employs event-related potentials (ERPs) to find evidence to support the underlying distinction into accented and unaccented stem types. The results of two EEG experiments using a stress violation paradigm reveal that Russian listeners are highly sensitive to changes of metrical structure and that prosodic manipulations may impede lexical retrieval. In the first experiment, in which the stimuli were not given prior to auditory presentation, metrical violations evoked a pronounced N400 effect for all stem types, and a late positivity for one of the stem types, indicating a difference in stress processing. In the second experiment in which the stimuli were visually introduced before auditory presentation, stress shifts to the second syllable induced late positive component (LPC) indicating an ease in the evaluation of the metrical form. Overall, the present findings partially support the division into lexically specified and unspecified Russian accent types. In addition, the results show a strong correlation between the patterning of ERP components and the direction of stress shift, suggesting a trochee to be the default foot type in Russian.
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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 | 0.001 |
| 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) | 0.001 | 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".