A STUDY OF IMPACT OF E-COMMUNICATION ON WRITTEN ENGLISH PRACTICED BY UNDERGRADUATE STUDENTS
Bibliographic record
Abstract
In present time, the phenomenon of ‘globalization’ has been largely possible due to increased popularity of e-communication and digitization of information. Additionally, the ready availability of computers and internet has contributed in ‘linking’ the world together into a single whole. It has transformed human life and environment; and has fulfilled the innate human desire for speed. And by using various e-tools, such as emails, social networking sites, mobiles, blogs and others, fast paced communication has been made possible. Consequently, the modern scenario of communication has changed forever. It has helped in improving the standard of living and has enabled faster, clearer and more accurate outcome to the designated tasks. The young generation is increasing moving towards using the e-mode for communication. The youth of India are becoming more and more techno friendly. They use e-tools like smart phones, laptops, tablets etc. excessively for interaction and communication. Moreover, use by doing research on the above mentioned topic the researchers wish to get an insight into an extremely current topic, namely, the connection between use of e-communication and the change in the method of communication and language, specifically written English, due to its wide-spread use. In order to make the research more pointed, the researcher has decided to study a specific target group of youngsters, namely, undergraduate students and evaluate their writing pattern. By carrying out research in the proposed topic, the researchers attempt to analyze the impact of e-mode on the English language.Moreover, they wish to specifically investigate the kind of impact e-communication has had on written English of these students: Whether it actually has had an impact or not; and to what extent it has transformed the English used by them or is it the other way around?
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".