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Record W2900621164 · doi:10.1075/itl.17021.dao

Structural alignment in L2 task-based interaction

2018· article· en· W2900621164 on OpenAlex
Phung Dao, Pavel Trofimovich, Sara Kennedy

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueITL Review of Applied Linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsConcordia University
Fundersnot available
KeywordsTask (project management)Computer scienceStructural alignmentRepetition (rhetorical device)Natural language processingArtificial intelligenceLinguisticsSequence alignmentEngineering

Abstract

fetched live from OpenAlex

Abstract This study investigated L2 structural alignment, the tendency for interlocutors to re-use a syntactic structure present in recent discourse, focusing on two information-gap interactive tasks. Thirty-four university students from diverse language backgrounds, recruited from different academic programs at a Canadian English-medium university, carried out the two information-gap interactive tasks in dyads. Interaction data were transcribed and coded for instances of structural alignment and the alignment’s characteristics in terms of structure type and accuracy. Results indicated that structural alignment occurred in L2 task-based interaction generated by both tasks. This structural repetition was linked to an improved accuracy of subsequent language production. Furthermore, the two tasks were associated with different structures that were converged on, and with varying degrees of structural alignment. These findings are discussed in terms of effects of task features on structural alignment, and the role of structural alignment in subsequent language production.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.303
Teacher spread0.269 · 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