Lexical changes in modern English: Abbreviations and shortened words formed under the influence of various social factors
Bibliographic record
Abstract
The relevance of research determined by the popular use of abbreviations and abbreviated words in discourses of the English language, as well as the need to analyse this phenomenon, which is constantly in the process of change. The purpose of this study: definition of the concept of abbreviation and abbreviated words, analysis of abbreviations in the modern English language, formed under the influence of various social factors. The method of systematic, logical and content analysis, the method of synthesis, analogy, and the method of deduction were used in the study of this topic. The article considers the definition of the main types, properties of abbreviations and abbreviated words, their classification, and role in speech, the main social factors that caused the formation of abbreviations are named, it is determined that the leading role in the activation and development of abbreviations in the modern English language is played by the process of global integration and rapid development of information technologies. This work examines 42 of the main types of abbreviations and abbreviations in modern English: the acronyms Radar, NASA, VIP, UNESCO, BBC, EEC, GMO, CCFF, EEB; initialisms imho, asap, OTT; Abbreviation of lab(oratory), exam(nation), cap(tain), vet(eran); initial abbreviations EFTA, EMC; abbreviations IVF, ESA, ASAP, AYOR, BAU, DIY, DM, FB, FYI, G2G, HIFW, IMO, JIC, LOL, MSG, OOO, RN, RT, TIA, TTYL, WDYT/WDYM, WFH, COVID-19, NCP, formed under the influence of various social factors. The practical significance of this article lies in the fact that the main provisions and the obtained results of the analysed material can be used in conducting classes in philology, linguistics and linguistics, devoted to abbreviations and shortened words.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".