Bibliographic record
Abstract
Social Media plays an important role, helps one communicate in group or with individuals through the internet. One very important mode is Facebook which is a vast network to connect to friends, relatives, colleges etc. It helps people to be in touch with their loved ones and it is helping lot of young people to look for jobs/employment avenues also. It also provides a platform to make groups and pages of likeminded people to share their thoughts or ideas. Many studies have found that social media and networking sites are acting as great medium for view mobilization and information also. Youth are raising their voice against anti-social acts like violation of human rights, corruption, exploitation, and some public issues to like drinking water crisis etc. These social networking sites are proving themselves an advantageous palter form; at least in collecting the opinion of people on these social issues. Youth are getting more aware about the social issues mainly through Facebook and Twitter. But the lacuna is that youth generally don't discuss these issues, they just share it or like it. Most people think that youth can play a positive role in changing our society which is represented in most of the responses to different queries in the study. The objective of the study is to analyse the impact of social media on youth empowerment. This study gives an insight about youth connection to social issues and the social networking site. The research also analyses whether youth really participates in the movements or just discusses them on the internet. This research has been conducted on the youth of Uttar Pradesh state in India on 400 respondents using the social sites regularly.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".