Information Communication Technology and Citizen Journalism in Nigeria: Pros and Cons
Bibliographic record
Abstract
This study explicates the relationship between citizen journalism and ICT in Nigeria. It explores the pros and cons of ICT and citizen journalism. Qualitative research method was employed for the collection of secondary data which comprised of books, magazines and journals. The study reveals that in as much as citizen journalism and ICT are interwoven, numerous issues and challenges associated with ICT are confronting the efficiency of citizen journalism in Nigeria. Blogging, podcasting and mablogging among others have made internet users (Netizens) to no longer passively consume media news but actively participate in them. Another issue confronting citizen journalism and ICT is the fact that there are no editors (gatekeepers) in the news and information disseminated through citizen journalism using the available ICT media. No editors to verify the truth within what someone has written, unlike in the traditional journalism and in the world of endless information, credibility is a very essential ingredient for information seekers. To curtail some of the issues affecting citizen journalism/participation, the study recommends that participants (citizen journalists) should try as much as possible to ensure that their news and information are edited by professionals before they are published. ICT facilities should be made available in areas where they are not available and at cheap cost to ensure that its range of targeted audience is vast, thus making it more efficient.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".