Expanding the Art of the Possible: Leveraging Citizen Journalism and User Generated Content (USG) for Peace in Sri Lanka
Bibliographic record
Abstract
At first blush, the media in Sri Lanka is diverse and multilingual with distribution and consumption of traditional media (e.g. TV, radio, print) spread over the island. Further examination reveals serious and growing challenges to impartial, accurate and responsible journalism. Journalists themselves rarely adhere to professional standards and ethics, or are often violently coerced into supine, submissive agents of government propaganda. There is not a single newspaper in Sri Lanka that is in Sinhala and Tamil. Journalists themselves tend to be monolingual. Lack of access to the embattled North and East and the stereotypes of the other result in biased, unprofessional reporting that fuels war (Deshapriya and Hattotuwa 2003 and 2005). The overarching problems of a state riven by violent conflict, corruption, nepotism and the significant breakdown of democratic governance and human rights, especially in recent years, deeply inform the timbre of traditional media. It is a vicious symbiosis – traditional media is both shaped by and shapes a violent public imagination. The potential of web 2.0 and new media in general and citizen journalism, mobile phones and USG in particular (e.g. You Tube videos, blogs, SMS and mobile sites) suggests that content that critiques the status quo , authored by civil society, can play a constructive and increasingly significant role in peacebuilding and stronger democratic governance in Sri Lanka. Through the example of Groundviews , Sri Lanka's first citizen journalism website, this chapter will interrogate the potentials and pitfalls of web and Internet activism in a country where political violence is an everyday reality.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".