Re-Evaluation of Nepali Media, Social Networking Spaces, and Democratic Practices in Media
Bibliographic record
Abstract
This chapter first analyzes the Nepali mainstream media and social media's effect upon its relationships with audiences or news-receivers. Then, it explores how social media is a virtual space for creating democratic forums in order to generate news, share among Networked Knowledge Communities (NKCs), and disseminate across the globe. It further examines how social media can embody a collective voice of indigenous and marginalized people, how it can better democratize mainstream media, and how it works as an alternative media. As a result of the impact of the Internet upon the Nepali society and the Nepali mainstream media, the traditional class stratifications in Nepal have been changed, and the previously marginalized and disadvantaged indigenous peoples have also begun to be empowered in the new ways brought about by digital technology. Social networking spaces engage the common people—those who are not in power, marginalized and disadvantaged, dominated, and excluded from opportunities, mainstream media, and state mechanisms—democratically in emic interactions in order to produce first-hand news about themselves from their own perspectives. Moreover, Nepali journalists frequently visit social media as a reliable source of information. The majority of common people in Nepal use social networking sites as a forum to express their collective voice and also as a tool or medium to correct any misrepresentation in the mainstream media. Social media and the Nepali mainstream media converge on the greater issues of national interest, whereas the marginalized and/or indigenous peoples of Nepal use the former as a space that embodies their denial of discriminatory news in the latter.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".