Assessment of Fisherfolk Information Seeking Behaviour with Mobile Phone for Improve Extension and Advisory Services
Bibliographic record
Abstract
The study assessed information seeking behaviour of fisherfolk with mobile phone in fishing communities around Kainji Lake basin, Nigeria. Primary data was generated through interview schedule from 165 respondents and analysed with descriptive and factor analysis. Result revealed that mobile phone improved information seeking behaviour of fisherfolk with associates in the fishing communities than with outsiders in government establishment. Kinds of information sought with mobile phone cut across economic, social and health issues in fish market, social gathering, fish catch/gear, health, weather and security related matters. Pattern of information seeking revealed regular use of close associates than extension workers. Close associates mostly contacted with mobile phone for information were fellow fisherfolk (64.2%), family members (64.8%) and community members (55.8%) but weak with fisheries institute (4.8%) and extension agents (0.6%). Fisherfolk rated voice call as the most effective medium in information seeking over flashing, SMS, voice message, video and pictures. Result of factor analysis categorised the challenges into poor financial status, inadequate knowledge and skill, poor network services and lack of energy to recharge mobile phone batteries. It is recommended that extension providers should use the established effective medium and pattern of information behaviour to package and disseminate messages to meet needs of fisherfolk in the lake basin. Interim measure is to provide tool free mobile lines to improve contact and trust to strengthen rapport. Also, mobile network operators should reduce tariff, improve quality of services as well as incorporate training in their social responsibility and promotion strategies for fishing communities to benefit.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".