Full Description
Based on real-life case studies, this book provides an empirical study of the linguistic accessibility of environmental information for people from diverse language, cultural, and educational backgrounds. It deploys well-established statistical and machine learning models to evaluate the key linguistic features of environmental information dissemination, to both English and non-English-speaking readers, on topics such as environmental health risk and natural disaster preparedness. Using Japanese, Swahili, Tigrinya, Zulu, and Somali languages as illustrations, this book shows how English-speaking professionals can significantly improve the cross-lingual translatability of community-oriented environmental information by using machine learning. It can be used as a latest research reference for readers from different disciplinary backgrounds interested in how to design linguistically accessible environmental information to increase its social and community impact. It can also be used as a practical guidebook to community-oriented environmental information design.
Contents
1. Previous research on language readability; 2. Statistics and machine learning for textual readability studies; 3. Readability of environmental health resources for parental education; 4. Exploring the suitability of environmental health information for parental education using machine learning models; 5. Improving the readability of Japanese translations of natural disaster risks through predictive automated English information design; 6. Forecasting mistakes in machine translation of environmental health information to African languages; 7. Assessing linguistic accessibility of Chinese environmental and health information; 8. Conclusion; Appendices; References; Index.
