Written Network Communication: Communicative Needs and Ambiguity of Interpretations
Bibliographic record
Abstract
Social networks are dynamic, accessible virtual planes of communication, through which users carry out the cross-border and transient exchange of information and emotions, meeting their communication needs. The article outlines the main aspects of written communication, its communicative needs, and the ambiguity of interpretations.The constant growth of the popularity of social networks with the simultaneous displacement of the importance of live, direct communication forces scientists in various fields, including the theory of communication, communicative linguistics, to rethink the phenomenon of "communication". It is about creating a special discourse, which is caused by the specifics of the communication channel/code between communicators and the virtual chronotype. Attention has been drawn to written communication on social networks in terms of speakers’ intentions, the interpretation of explicit and implicit information, its impact on the participants of communication, the formation of the special culture of communication (non-library), etc.Specificity in the written form of communication in the social media has been caused by many factors: the readiness or unreadiness of the virtual interlocutor to interact; dominance of visual perception, hence, special attention of recipients to the layout, structuring of the text, volume, photo accompaniment, etc.; knowledge of the latest trends in online communication (fashion clichés, abbreviations, slang, memes); replacement or substitution of non-verbal means by various means of paragraphemics, for example, smilies, stickers, pictures, animation, etc.; developing of a specific network chronotype, in which there are no time limits and which is constantly expanding in volume due to the multiplicity of accounts, texts, chats, groups.Not everyone who communicates on social media knows the intentions of other people. On the one hand, such communication generates the selectivity or concealment of true meanings in the author's texts and on the other hand, additional meanings are taken on during the recipients’ interpretation.
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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 | 0.018 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".