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Record W4390343785 · doi:10.5430/wjel.v14n2p56

It’s Nice to Meet You: Contextual Configuration on Formal and Informal Introductions

2023· article· en· W4390343785 on OpenAlexvenueno aff
Agus Riyanto, Ardik Ardianto

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsSituational ethicsConversationContext (archaeology)LinguisticsDe factoInterpretation (philosophy)Context analysisConversation analysisComputer scienceSociologyProcess (computing)PsychologySocial psychologyPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

The discourse of introduction is a discourse exploiting full of contextual meanings. By means of two approaches, viz. situational context and contextual configuration, this paper aims to delve deeper into the discourse of formal and informal introductions in everyday English conversation. Both discourses are taken from the book Everyday Conversation. The data were analyzed from two paradigmatic approaches, i.e., situational context with the components of field, tenor, and mode, and contextual configuration with the three main maxims of OOI (obligatory, optional, and iteration). Based on the careful analysis, the findings demonstrate that both introduction discourses, whether formal or informal ones, are built on the foundation of two types of elements, namely obligatory and optional, which are interwoven within a potential common structure of the following contextual configuration: (OE). [IR^IG1^IG2]. (IE)(context-dependent) > [(R)^(RC)]. Ultimately, through this paradigmatic approach to the discourse of introduction, it arrives at an understanding that the relationship between language and its contextual use is that of mutuality; shall language need context in the process of interpretation, the context ipso facto needs language to manifest per se.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.282
Teacher spread0.255 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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