The Role of Mass Media in Rural Information System in Nigeria
Bibliographic record
Abstract
The article examined rural reporting in Nigeria and discovered that the only thing that constitute news for reporters in the country is only when a strange thing negative happens in the rural areas. For example, when there is ritual sacrifice, community clashes, rape, murder, etc.. The press hardly reports any good news about the rural communities in Nigeria. For example, when there is peace, harmony and self-efforts at rural development undertaken by the initiative of the rural community leaders, it is hardly given attention by the press reporters in Nigeria. Consequently, rural poverty continues to increase unabated but the Nigerian press reporters could not effectively expose the deplorable conditions under which the rural dwellers live in. The bias of the reporters is in favour of the urban dwellers who are adjudged to be learned, enlightened and understand the meaning and importance of news. The reporters argue that if the news reported upon is not essentially urban oriented, the patronage especially in the print-media would be very low. The study concluded by observing that the trend should be reversed immediately. In fact the news reporters in Nigeria should show more patriotism in the coverage of events in the rural areas. This is the only way government could know and understand the plights of the rural dwellers for effective public policy-making to reduce the present level of rural poverty and reverse the current rise in rural-urban migration in the country.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".