DIMENSIONS OF RADIO COVERAGE AND CONTENT GENERATION OF AGRICULTURAL BIOTECHNOLOGY NEWS IN KENYA
Bibliographic record
Abstract
Abstract This paper presents findings of a just concluded research designed to better understand radio usage in communicating the newly emerging field of agricultural biotechnology in Africa. While various national and international fora have acknowledged the importance of mass media in shaping perceptions and informing decision-making processes, very little has been done to gauge dimensions of coverage and the whole spectrum of content generation and capacities needed in respect to agricultural biotechnology. Quantitative and qualitative content analysis of the coverage of biotechnology issues in nine radio stations and five newspapers in Kenya over a period of one year was conducted. The articles and programmes were written or presented during a period when the country was experiencing heightened media coverage of biotechnology due to debates on enactment of a Biosafety Bill to regulate modern biotechnology. Findings revealed that agricultural biotechnology is not adequately covered by Kenyan media in a way that could enable informed public debate and policy choices. This was demonstrated by few number of items presented, little space allocated and placement of the stories in the newspapers. Radio producers cited various challenges that hindered adequate coverage of biotechnology which included: their low scientific knowledge, scientists’ use of technical jargon and unavailability of experts well versed and confident to speak in local languages. Measures should be taken to improve both quantity and quality of coverage of biotechnology issues by improving relationship between journalists and scientists. Production of a local glossary of biotechnology terms in local languages could greatly enhance confidence of radio producers and presenters. Training of journalists to increase accuracy of coverage and that of scientists on science communication skills cannot be overemphasised. Key Words: Agri-Biotechnology, Radio, GMOs, Communication, Mass Media Acknowledgment This research was supported by the International Development Research Center (IDRC) of Canada JCMRJournal of Communication and Media Research, Vol. 3, No. 2, October 2011, 13 – 27. © Delmas Communications Ltd. About the authors *Dr. Margaret Karembu and Faith Nguthi are with the International Service for the Acquisition of Agri-Biotech Application (ISAAA Africenter), Nairobi, Kenya. **Toepista Nabusoba is with the Kenya Broadcasting Corporation, Nairobi, Kenya. ***Peter Oriare is with the University of Nairobi’s School of Journalism and Mass Communications, Nairobi, Kenya. ****Julius Nyangaga is with the International Livestock Research Institute. *****Heidi Schaeffer is with Rhythm Communications, Canada. ******Mary Myers is a Development Communication Consultant in the United Kingdom. Full Article Words: 6,349; Pages: 15
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".