It’s Nice to Meet You: Contextual Configuration on Formal and Informal Introductions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".