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Record W2884198204 · doi:10.1075/tblt.10.05rea

Task modality effects on Spanish learners’ interlanguage pragmatic development

2018· book-chapter· en· W2884198204 on OpenAlexaff
Derek Reagan, Caroline Payant

Bibliographic record

VenueTask-based language teaching · 2018
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsInterlanguageLinguisticsPsychologyModality (human–computer interaction)Task (project management)Computer scienceCognitive psychologyArtificial intelligenceEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Abstract Within the field of second language acquisition (SLA), we have witnessed a rise in research on task-based language teaching (TBLT) and its effects on L2 development ( Kim, 2015 ). However, few studies have examined how TBLT could facilitate the development of interlanguage pragmatics ( Taguchi & Kim, 2015 ), an issue which this volume aims to address. Moreover, whether task modality (i.e., oral versus written tasks) mediates development has yet to be investigated. The current study with learners of Spanish focused on the effects of using pedagogical tasks and on the manipulation of task modality on learners’ L2 pragmatic competence through the production of Spanish requests and speech act modifications. Two intermediate classes ( n = 25) of Spanish completed either an oral or written story completion task. Drawing on oral and written Discourse Completion Tests, we found that tasks positively impacted learners’ production of L2 requests. However, significant differences between modality groupings were not identified.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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