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Record W2857481617 · doi:10.5539/ijel.v8n5p272

Pragmatics of English Speech Acts: Compliments Used by Macedonian Learners

2018· article· en· W2857481617 on OpenAlexvenueno aff
Marjana Vaneva, Marija Ivanovska

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMacedonianPragmaticsCompetence (human resources)PsychologyLinguisticsLinguistic competencePerceptionForeign languageComputer sciencePedagogy

Abstract

fetched live from OpenAlex

This study investigated the pragmatic knowledge and competence of the Macedonian learners of English, i.e. Macedonian high school English students’ views on and perceptions of pragmatics, their pragmatic competence in the speech act of complimenting, and the language learning strategies employed in the process of acquiring pragmatic knowledge.Although “student-oriented” and “evaluation-oriented”, the teaching methods currently used do not sufficiently develop students’ communicative competence in the process of English teaching and learning. Many students lack pragmatic knowledge of how to use the foreign language in specific settings and how to interpret certain utterances used by native speakers of the other language.Despite all the efforts made to improve the English language education in Macedonia, yet greater emphasis should be put on students’ linguistic and pragmatic competence in the English teaching and learning process. This area is the focus of the current study that analyses the English speech act of complimenting and its use by the Macedonian learners of English.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.036
GPT teacher head0.319
Teacher spread0.283 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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