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Record W2521116856 · doi:10.5539/ijel.v6n5p82

A Comparative Study of Request Speech Acts in Badrudi, Persian, and English

2016· article· en· W2521116856 on OpenAlexvenueno aff
Somayyeh Daneshpazhuh, Mohsen Shahrokhi

Bibliographic record

VenueInternational Journal of English Linguistics · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPersianPsychologySpeech actSample (material)Realization (probability)Power (physics)LinguisticsSocial psychologyComputer scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

<p>This study was an attempt to delve into how requestive speech acts were realized in English, Persian, and Badrudi by EFL Persian students at the upper-intermediate level who spoke Badrudi as well. To collect the required data three versions of a Discourse Completion Test in English, Persian and Badrudi were administered to the research sample (N=40). The collected data was codified and analyzed with regard to social power and distance as two contextual variables and further statistical procedure was run to sustain or reject the research hypotheses. The analysis of the data revealed that the presence of power in interactions influences the request realization by the participants as there were differences among the most frequent strategies in English, Persian, and Badrudi. Moreover, with respect to the presence of social distance the comparison among request strategies in English, Persian, and Badrudi indicated the participants opt for different strategies as the most frequent strategies. The results also revealed that with the presence of power and distance direct strategies were the most frequent ones, conventionally indirect ranked second and indirect strategies were used as the least frequent strategies in English, Persian, and Badrudi.</p>

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)0.000
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.014
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)0.0000.000

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 head0.054
GPT teacher head0.333
Teacher spread0.279 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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