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Record W1834813043

Request Strategies by Second Language Learners of English: Pre- and Post-head Act Strategies

2012· article· en· W1834813043 on OpenAlexvenueno aff
Saad Al‐Gahtani, Saad A. Alkahtani

Bibliographic record

VenueStudies in literature and language · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySpeech actInterlanguageHead (geology)PragmaticsPower (physics)Linguistics
DOInot available

Abstract

fetched live from OpenAlex

This study investigates the speech act of request by Saudi high- and low-level learners of Australian English. All participants were asked to take part in three different role plays, which varied according to the relative power relationship between the informant and the conductor. We found that high-level learners did not considerably differ from low-level learners in terms of pre- and post-head act strategies, and request strategies; thereby indicating that proficiency level does not have a significant impact on L2 learners’ choice of pre- and post-head act strategies and request strategies. However, both groups of learners deviated from Australian English native speakers in terms of post-head act and request strategies. In light of the social variable (power) influence, it was found that power affected both groups of learners, along with the native speaking group, in terms of pre- and post-head act strategies. However, power did not have an impact on the SLL group, while it did have an effect on the high-level group, along with the native speaking group, in terms of request strategies. Thus, there is no apparent correlation between the social variable (power) and L2 learners’ use of pre- and post-head act strategies, while power positively correlates with L2 learners’ proficiency level regarding their use of request strategies. Key words: Interlanguage pragmatics; Speech act of requests; L2 learners

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.290
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
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 head0.021
GPT teacher head0.322
Teacher spread0.301 · 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.

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

Citations6
Published2012
Admission routes1
Has abstractyes

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