The Impact of Social Media and Electronic Literature on Literary Studies, Language Learning and Acculturation: A Study Conducted in South India
Bibliographic record
Abstract
This research aims to explore the impact of social media and electronic literature on young individuals' lives, especially in literary studies, foreign language learning and acculturation. The samples for the study were taken from young people in selected colleges in three South Indian states namely Kerala, Tamil Nadu and Karnataka. The objectives of this study were to determine the role of social media in the day-to-day life of youngsters, analysing major reasons behind social media usage, the role of social media and electronic literature in literary studies, foreign language learning and teaching, its impact on education and moulding language skills, and the extent to which it influences social change and acculturation. For the data collection, a structured survey questionnaire was prepared and distributed among the students. Purposive sampling was the sampling strategy used to collect the data and 355 samples were collected. A five-point Likert scale was used in the questionnaire. The researcher employed various statistical tools and methods like percentage analysis, graphical analysis, and descriptive statistics to have a proper analysis of the collected data. Qualitative analysis was carried out using discourse analysis and grounded theory analysis. It is observed that social media and digital technology have a pivotal impact on the lifestyle of the youngsters. Digital media act as a means for English language development and electronic literature helps to enhance the mode of language teaching and make the classes interesting. Electronic literature tremendously supports literary studies, education and related initiatives.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".