Semantic and Cognitive Communicative Aspects of Abbreviation in the Modern English Discourse Varieties
Bibliographic record
Abstract
The paper focuses on the integrated approach to investigation of abbreviations that are referred to the units of secondary nomination. Abbreviations facilitate the communication process due to the principle of economy in language. Abbreviation that goes back to ancient times is one of the most actively performed and intensive processes in the English language. The use of abbreviations is connected with both linguistic and extra-linguistic factors that to a great extent determine a variety of discourse under consideration and ways of decoding certain linguistic units of abbreviated form, where context plays the most important role. There exists a number of structural types or models, according to which abbreviations are created in order to function as separate linguistic units used in the communicative process, where in the course of time they tend to acquire new independent and sometimes unique semantic properties. In socially determined professional registers certain regularities of abbreviations constructing and functioning are observed. In this paper most of the examples refer to such discursive varieties as politics, public relations, business and to a less extent medicine. Interdiscursivity that in this particular context means actualisation of the same abbreviated form within different discursive varieties seems to be one of the basic features of the abbreviation process under analysis. From the cognitive point of view, the study of abbreviated forms is based on the theory of propositional structures in the human mind manifestation. The cognitive approach correlates with linguistic semiotic and general semiotic issues, as at the iconic level of representation an abbreviation can be considered a starting point for creation of new entities and images, based on the results of perception, and the object for subsequent interpretation. This involves operation with fragments of reality, subject to reflection in the domain of human consciousness. Cognitive communicative approach takes into account different formats of knowledge that present the foundation for abbreviations active use and distribution. The Internet as a new medium for the language existence, as well as application and formation of human thought, plays an important role in optimizing messages as well as the communication process as a whole due to abbreviation techniques.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".