Stress in Context: Morpho-Syntactic Properties Affect Lexical Stress Assignment in Reading Aloud
Bibliographic record
Abstract
Recent findings from English and Russian have shown that grammatical category plays a key role in stress assignment. In these languages, some grammatical categories have a typical stress pattern and this information is used by readers. However, whether readers are sensitive to smaller distributional differences and other morpho-syntactic properties (e.g., gender, number, person) remains unclear. We addressed this issue in word and non-word reading in Italian, a language in which: (1) nouns and verbs differ in the proportion of words with a dominant stress pattern; (2) information specified by words sharing morpho-syntactic properties may contrast with other sources of information, such as stress neighborhood. Both aspects were addressed in two experiments in which context words were used to induce the desired morpho-syntactic properties. Experiment 1 showed that the relatively different proportions of stress patterns between grammatical categories do not affect stress processing in word reading. In contrast, Experiment 2 showed that information specified by words sharing morpho-syntactic properties outweighs stress neighborhood in non-word reading. Thus, while general information specified by grammatical categories may not be used by Italian readers, stress neighbors with morpho-syntactic properties congruent with those of the target stimulus have a primary role in stress assignment. These results underscore the importance of expanding investigations of stress assignment beyond single words, as current models of single-word reading seem unable to account for our results.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".