This volume is complemented by the sister volume Dangerous Language — Esperanto under Hitler and Stalin which offers a concentration on the creation and early emergence of Esperanto as an international language.

This volume is complemented by the sister volume Dangerous Language - Esperanto and the Decline of Stalinism which offers a concentration on the Cold War history of Esperanto in Eastern Europe.

This collection represents the first of its kind, bringing together the latest research on danmu, the fast-growing phenomenon of live comments overlaid on audiovisual media changing the shape of audience participation on video-streaming platforms and of audiovisual translation research today.

This collection brings together leading and emerging scholars across Dante studies and translation studies to chart the English-language translation journey of the Divine Comedy from the 18th century to the present.

This book explores the application of Data and Model Centric AI for Industry 5.0 Solutions, vital in the evolving manufacturing landscape. It delves into data-centric AI, focusing on data collection, processing, and analysis for informed decision-making and operational optimisation.

An essential guide to frequently used data collection techniques in the field of applied linguistics.

An essential guide to frequently used data collection techniques in the field of applied linguistics.

This Element is a practical guide for language teachers and graduate students intending to explore or upgrade their use of corpora in the language classroom and beyond. In today's context, the use of corpora emerges as a prime resource, developing language awareness, noticing skills and critical thinking for learning.

Illustrated with case studies throughout, this book shows that digital tools can provide exciting, novel answers to old questions in English linguistics. With an emphasis on methodological transparency, this book is essential reading for both scholars and students working in English linguistics, especially those using data-intensive approaches.