When Knowing Grammar Depends on Knowing Vocabulary: Native-Speaker Grammaticality Judgements of Sentences with Real and Unreal Words
Bibliographic record
Abstract
Abstract: This study examined how the presence of real versus unreal words in sentences affected the ability of native English speakers to make accurate grammaticality judgements and forced-choice decisions for sentences with violations in the use of dative alternation and comparatives. Sentences with dative alternation violations contained polysyllabic verbs (*John explained Mary the plan) that were real (e.g., explained), similar (e.g., explunned), and dissimilar (e.g., tidnopped) to real verbs. Sentences with comparative violations contained polysyllabic adjectives (*Robert is demandinger than Allen) that were real (e.g., demanding), similar (e.g., demunding), and dissimilar (e.g., natormunt) to real adjectives. Accuracy of grammaticality judgements was much lower for sentences with unreal words than real words. For sentences with comparatives, accuracy also was higher in sentences with similar words than with dissimilar words, demonstrating a graded effect for partial access. These findings provide support for theoretical accounts that associate knowledge of these structures with knowledge of real words and for instruction oriented toward the development of vocabulary knowledge.
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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 | 0.005 | 0.050 |
| 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 | 0.001 | 0.001 |
| 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".