Striking a Balance between Science and Arts: Mass Media Dilemma in Reporting Health and Environmental Issues
Bibliographic record
Abstract
Not many people realize that the mass media carry two significant values in their content. One is the value of arts that very much represents the subjective thoughts; and the other is the value of science, which emphasizes precision, accuracy and accountability. Both values reside in the content of the mass media that very much become a precursor and inspiration for government and society to achieve their goals. Nevertheless, both are contradictory in nature. From the general semanticist point of view the words of science could bring objectivity of the mass media to achievable heights. Whilst the words of arts, which are emotion laden yet exhilarating, could lead to human prejudices or perhaps human enlightenment. Although extremely subjective in nature, the arts denote the artistic creation of man that without them, the mass media could hardly persist. On the other hand, news and information of scientific in nature such as pollution, natural catastrophes, diseases and medical discoveries as well as environmental disturbances highlighted by the mass media are seldom being disputed. Hence, based on a content analysis study of four Malaysian mainstream newspapers, this paper will dwell into the plight of the Malaysian print media in trying to create a balance between arts and science especially in communicating health and environmental issues. From a general semantics perspective, the paper will also look at the use and misuse of words by print media practitioners in imparting arts and science messages.
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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 | 0.038 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".