The Impact of Social Media on Conventional Journalism Practice in Nigeria: A Study of Journalists’ in Jalingo Metropolis
Bibliographic record
Abstract
This study examines the impact of social media on journalism practice in Nigeria. It explores the extent to which social media has changed journalism practice in Nigeria with special reference to Journalists in Jalingo metropolis. The quantitative survey method was adopted for the study. The population is comprised of 293 Journalists in Jalingo metropolis registered under Nigerian Union of Journalists. The study employed Taro Yame’s formula to sample out 75 journalists. Questionnaires were used as the tool for data collection. The researcher administered questionnaires to 75 purposively selected Journalists, and 70 were duly answered and retrieved. Data gathered were analyzed with SPSS version 20 with devices such as frequency counts and simple percentages. Data analyzed were presented in tables. Findings revealed among other things that a considerable number of journalists in Jalingo metropolis are computer literate, and they have internet access at various levels. Findings also revealed that journalists in Jalingo metropolis go online very often and that they prefer Facebook to other forms of social media and this assist them in faster gathering and dissemination of news. The study recommends among other things that Journalists should conduct researches on the accessible online networking organizing apparatuses to check which one of them is more and solid, keeping in mind the end goal to guarantee the validity of sources.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".