The Mass Media Reportage of Crimes and Terrorists Activities: The Nigerian Experience
Bibliographic record
Abstract
The new mass media technologies now make information processing and distribution more accessible to people globally. Marshall Mcluhan’s “global village” has given birth to a “global palour”. However, perpetrators of crimes now bask on the philosophy of communication media practitioners that people have the right to know what is happening within and outside their environment. This stance is rapidly dismantling, in an amazing fashion, the hitherto accorded respect for media ethics. Neil Postman, a New York media analyst, describes the creator of technology as the list judge of its consequences, especially with regards to the technology of the media. True, every communication medium is potent with the possibility of occasioning other consequences not directly intended by it. This paper, therefore, attempts to bring to the fore the way communication media are inadvertently promoting crimes and terrorist activities globally. It is the stand of this paper that a global overhaul of mass communication media is needed for balance reportage that would bring about global and meaningful developments of human and material resources under an atmosphere of peace and mutual tolerance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".