Reflection on Some Socio-Economic Factors That Shaping the Evolution of Mass Communication in Nigeria
Bibliographic record
Abstract
Mass Communication as a field of discipline has undergone several evolutions from one stage to the other in the course of its existence. One reason is because different fields have been incorporated into Mass Communication to make it a coherent field of study. Such fields include mechanics, electronics, mathematics, economics, psychology, sociology, just to mention a few. Whatever evolutions that have transpired in these fields have had great impact on Mass Communication. From the early 1900s, the rise of Mass Communication followed a pattern of industrial revolution following every subsequent revolution in media technology. The media were among the many technologies that shaped the world and were shaped by other as well.. From the oral tradition, the print, the rise of the electronic media and ultimately to the digital-networked communication, Mass Communication has undergone changes from diverse paradigms. The field of Mass Communication is an ever-changing field with notable changes taking place almost on a daily basis. In this paper, we focus on a number of economic factors that shaped, (and still continue to shape) the epochs and annals of Mass Communication in Nigeria. This paper identifies the political climate, advertising, news commercialization, and entertainment as some of the identifiable socio-economic factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".