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Record W7014377713

Parsing Ambiguous Relative Clauses in L2 English: Learner Sensitivity to Prosodic Cues

2020· article· en· W7014377713 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2020
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSensitivity (control systems)ParsingComponent (thermodynamics)ProsodyGrammar
DOInot available

Abstract

fetched live from OpenAlex

We investigate effects of prosodic cues on interpretation of ambiguous sentences containing relative clauses in English by Spanish-speaking learners. English and Spanish differ in default preference for RC attachment: English has a weak low attachment (LA) preference (RC modifies NP2); Spanish has a stronger high attachment (HA) preference (RC modifies NP1). We conducted an interpretation task with auditorily-presented stimuli to examine whether prosodic cues determine attachment. Target items were manipulated for position of break and length of RC, NP1, and NP2. For both groups, break and length are significant. For the learners, proficiency interacts with break suggesting L1 transfer: lower proficiency learners choose HA more when break points to LA; higher proficiency learners choose HA more when break points to HA. Lower proficiency learners are more likely to choose LA overall, suggesting a recency effect. Our results confirm the importance of using aural stimuli when testing interpretation of ambiguous sentences.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.054
GPT teacher head0.306
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2020
Admission routes1
Has abstractno

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