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Record W4368375623 · doi:10.55593/ej.27105a2

Formulaic Language in the Acquisition of L2 Pragmatic Competence in a Community-based Classroom

2023· article· en· W4368375623 on OpenAlexaffabout
Alisa Zavialova

Bibliographic record

VenueTeaching English as a Second or Foreign Language--TESL-EJ · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsCarleton University
Fundersnot available
KeywordsUtterancePragmaticsCompetence (human resources)LinguisticsSpeech actPsychologyComputer sciencePedagogyMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

Pragmatic formulas have been recognized as linguistic building blocks necessary for successful speech act performance. Current approaches to speech act teaching overlook pragmatic formulas, promoting an incomplete view of pragmatics instruction. This paper reports on the results of a classroom-based study in which a formula-enhanced treatment focusing on both pragmalinguistic and sociopragmatic components of pragmatic ability was tested. Seven students from the Language Instruction for Newcomers to Canada (LINC) program participated in four lessons involving pre-, post- and delayed post-test measures. During the treatment, the students were exposed to target formulas from four interaction contexts. A qualitative utterance analysis was conducted to determine how pragmalinguistic and sociopragmatic abilities of the students evolved after the teaching intervention. Additionally, three expert judges evaluated students’ pragmatic performance. The results indicate that improvements in both pragmalinguistic and sociopragmatic abilities of the students were associated with the use of target-like formulas in their speech acts.

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 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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations1
Published2023
Admission routes2
Has abstractyes

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