MétaCan
Menu
Back to cohort
Record W2158773143 · doi:10.1017/s0272263115000297

LEXICAL PROFILES OF COMPREHENSIBLE SECOND LANGUAGE SPEECH

2015· article· en· W2158773143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStudies in Second Language Acquisition · 2015
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsConcordia UniversityWestern University
Fundersnot available
KeywordsLexisLinguisticsLexical choiceFluencyLexical densityPsychologyLexical itemVocabularyVariation (astronomy)LexicologyComputer scienceNatural language processing

Abstract

fetched live from OpenAlex

This study examined contributions of lexical factors to native-speaking raters’ assessments of comprehensibility (ease of understanding) of second language (L2) speech. Extemporaneous oral narratives elicited from 40 French speakers of L2 English were transcribed and evaluated for comprehensibility by 10 raters. Subsequently, the samples were analyzed for 12 lexical variables targeting diverse domains of lexical usage (appropriateness, fluency, variation, sophistication, abstractness, and sense relations). For beginner-to-intermediate speakers, comprehensibility was related to basic uses of L2 vocabulary (fluent and accurate use of concrete words). For intermediate-to-advanced speakers, comprehensibility was linked to sophisticated uses of L2 lexis (morphologically accurate use of complex, less familiar, polysemous words). These findings, which highlight complex associations between lexical variables and L2 comprehensibility, suggest that improving comprehensibility requires attention to multiple lexical domains of L2 performance.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0850.000

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 head0.057
GPT teacher head0.384
Teacher spread0.326 · 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