Lexical changes in modern English: Abbreviations and shortened words formed under the influence of various social factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it