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.
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Using real case studies, this book shows how to evaluate and enhance the linguistic accessibility of critical environmental information.
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.
Meng Ji is Professor at the School of Languages and Cultures, the University of Sydney, Australia. For two decades, her work has pioneered translation research innovation, through developing interdisciplinary research methods and models. Michael Oakes is Visiting Assistant Professor at the University of Birmingham, UK. His research interests are in Statistics for Corpus Linguistics, Authorship Attribution and Empirical Translation Studies.
