Resolving syntactic–semantic conflicts: comprehension and processing patterns by deaf Chinese readers
Bibliographic record
Abstract
The current study combined sentence plausibility judgment and self-paced reading tasks to examine the comprehension strategies and processing patterns of Chinese deaf individuals when comprehending written Chinese sentences with syntactic-semantic cue conflicts. Similar to findings from previous crosslinguistic studies on deaf readers, the Chinese deaf readers showed great variability in their comprehension strategies, with only 38% robustly relying on syntactic cues. Regardless of their overall comprehension preferences, the deaf readers all showed additional processing efforts as reflected by longer reading time at the verb regions when they relied on the syntactic cues. Those with less robust reliance on syntactic cues also showed longer reading time at the verb regions even when they relied on the semantic cues, suggesting sensitivity to the syntactic cues regardless of the comprehension strategy. These findings suggest that deaf readers in general endure more processing burden while resolving conflicting syntactic and semantic cues, likely due to their overall high reliance on semantic information during sentence comprehension. Increased processing burden thus may contribute to an overall tendency of over-reliance on semantic cues when comprehending sentences with cue conflicts.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".