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Record W4226062757 · doi:10.1075/bct.121.08tro

Second language comprehensibility as a dynamic construct

2022· book-chapter· en· W4226062757 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

VenueBenjamins current topics · 2022
Typebook-chapter
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of CalgaryConcordia University
Fundersnot available
KeywordsConstruct (python library)PsychologyTask (project management)Cognitive psychologyLinguisticsComputer science

Abstract

fetched live from OpenAlex

Abstract This study examined longitudinal changes in second language (L2) interlocutors’ mutual comprehensibility ratings (perceived ease of understanding speech), targeting comprehensibility as a dynamic, time-varying, interaction-centered construct. In a repeated-measures, within-participants design, 20 pairs of L2 English university students from different language backgrounds engaged in three collaborative and interactive tasks over 17 minutes, rating their partner’s comprehensibility at 2–3 minute intervals using 100-millimeter scales (seven ratings per interlocutor). Mutual comprehensibility ratings followed a U-shaped function over time, with comprehensibility (initially perceived to be high) being affected by task complexity but then reaching high levels by the end of the interaction. The interlocutors’ ratings also became more similar to each other early on and remained aligned throughout the interaction. These findings demonstrate the dynamic nature of comprehensibility between L2 interlocutors and suggest the need for L2 comprehensibility research to account for the effects of interaction, task, and time on comprehensibility measurements.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0190.000

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 head0.038
GPT teacher head0.313
Teacher spread0.274 · 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