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Record W2970333364 · doi:10.5539/ijel.v9n5p153

The Closing Sequences and Ritual Expressions of Informal Mobile Phone Calls Between Saudis: A Conversational Analysis

2019· article· en· W2970333364 on OpenAlexvenueno aff
Mohammad Mahzari

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
FundersDeanship of Scientific Research, Prince Sattam bin Abdulaziz UniversityPrince Sattam bin Abdulaziz University
KeywordsClosing (real estate)ConversationLandlinePhoneMobile phoneArabicTerminal (telecommunication)LinguisticsComputer scienceCommunicationExpression (computer science)PsychologyAdvertisingTelecommunicationsBusiness

Abstract

fetched live from OpenAlex

Although much work has been conducted on studying conversational openings of telephone and ritual expressions, conversational closings and ritual expressions have received less attention by researchers due to the complexity and difficulty of identifying the beginning of closings in telephone conversations. The parts of closing and ritual expressions on telephone have been examined in some languages, but Arabic has not been studied in landline telephone or mobile phone. Therefore, this study seeks to identify the sequences and ritual expressions between Saudi friends and relatives to explore the strategies of closing informal mobile phone calls by using a conversation analysis approach. Thirty audio-recorded and transcribed mobile phone conversations served as the data source for this study. The results found that the majority of mobile phone closing conversations include three parts: pre-closing, leave taking, and terminal exchange that are similar to many languages such as English, Japanese, and German. Also, various expressions were used in pre-closing and leave taking sequences, but the expressions of using prayers were used more frequently in the sequences. Finally, closing conversation is affected by various external and internal social factors in the sequences and the use of ritual expressions.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 head0.023
GPT teacher head0.299
Teacher spread0.277 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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