Quantification Scope Ambiguity Resolution: Evidence from Persian and English
Bibliographic record
Abstract
This study investigates the interpretation of scopally ambiguous sentences containing noun phrases with double quantified constituents from a processing perspective. The questions this study tried to answer were: whether or not the preferred interpretation for doubly quantified ambiguous sentences in English was influenced by English learners' L1 scope interpretation possibilities; whether or not the preferred interpretation of ambiguous sentences with double quantificational noun phrases was driven by surface configurations of sentences in English and Persian for SL learners of English either in off-line or on-line reading; and finally, whether or not the referential contexts of ambiguous sentences with double quantificational noun phrases in English and in Persian guided interpretation preferences for SL learners in off-line and on-line readings. Using an off-line judgment task and an on-line truth-value judgment task combined with a self-paced reading technique, the data of the study were collected from the Persian speakers and English learners. The results obtained from offline tasks indicated that the interpretation preferences of L2 learners of English were influenced by their background language. However, such influences were not found in the self-paced study. Furthermore, it was indicated that the surface configurations of sentences strongly affected L2 learners’ interpretation preferences favoring isomorphic interpretation for the universal quantifiers included in the subject position. Concerning the semantically referential-context effect on ambiguity resolution, the collected data revealed that context played a crucial role in the processing of scope ambiguity since the RTs for acontextualized stimuli were more than for contextualized ones in constructions with QNP-QNP in Persian and English.Key words: Quantification, Scope Ambiguity, On-line Tasks, Off-line Tasks
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".