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Record W2108922042 · doi:10.5539/ijel.v2n1p71

Giving Condolences by Persian EFL Learners: A Contrastive Sociopragmatic Study

2012· article· en· W2108922042 on OpenAlexvenueno aff
Laila Samavarchi, Hamid Allami

Bibliographic record

VenueInternational Journal of English Linguistics · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPersianLinguisticsPsychologyCompetence (human resources)Significant differenceContrastive analysisMathematicsPhilosophySocial psychology

Abstract

fetched live from OpenAlex

Within Second Language Acquisition (SLA) research area, speech acts studies are often conducted to measure SL learners' pragmatic (in)competence. Unlike other speech acts, the speech act of giving condolences has not yet been the subject of cross-linguistic or cross-cultural studies across Persian and other languages. This initiative study attempts to investigate a comparative analysis of giving condolences across English and Persian. To this end, an English 15-item Discourse Completion Task (DCT) was given to 10 native speakers of English and to 50 Iranian EFL learners who were also given the Persian version of the DCT for the purpose of comparison. The results of a prior pilot study had indicated a significant difference between the two groups. The results of the main study also indicated a difference between the two groups as such some of the Persian EFL learners socioculturally transferred this speech act from their L1 into L2 while some others did not. On the whole, Persian EFL learners were more direct than the English natives while offering their condolences.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.317
Teacher spread0.284 · 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 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

Citations28
Published2012
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicLanguage, Discourse, Communication StrategiesFrench-language works237,207