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Record W4410378789 · doi:10.5539/ijel.v15n3p61

Parsing Ambiguity: Garden-Path Sentence Processing in Italian ESL Learners

2025· article· en· W4410378789 on OpenAlexvenueno aff
Nicoletta Simi

Bibliographic record

VenueInternational Journal of English Linguistics · 2025
Typearticle
Languageen
FieldComputer Science
TopicLinguistic Studies and Language Acquisition
Canadian institutionsnot available
Fundersnot available
KeywordsAmbiguityParsingSentencePath (computing)Sentence processingComputer scienceNatural language processingLinguisticsArtificial intelligenceSpeech recognitionProgramming languagePhilosophy

Abstract

fetched live from OpenAlex

This study investigates how Italian learners of English as a second language (ESL) process garden-path sentences, with a focus on parsing strategies and comprehension across different CEFR proficiency levels. Participants completed a self-paced reading task, and their performance was assessed through both reading times and comprehension accuracy. The results indicate that advanced learners (C1–C2) demonstrate greater sensitivity to syntactic ambiguity and adjust their reading strategies accordingly, particularly when reanalysis is required. Intermediate learners (B1–B2), by contrast, exhibit more uniform reading patterns across conditions and appear less aware of syntactic cues, leading to lower overall accuracy. Interestingly, sentence length and structure influenced the two groups differently, suggesting distinct processing strategies based on proficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.047
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.287
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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