Routledge A Level English Guides equip AS and A2 Level students with the skills they need to explore, evaluate, and enjoy English. Books in the series are built around the various skills specified in the assessment objectives (AOs) for all AS and A2 Level English courses. Focusing on the AOs most relevant to their topic, the books help students to develop their knowledge and abilities through analysis of lively texts and contemporary data. Each book in the series covers a different area of language and literary study, and offers accessible explanations, examples, exercises, summaries, suggested answers and a glossary of key terms. Comparing Texts: provides students with the skills they need to compare and contrast texts explores and compares texts from a wide range of genres and periods draws on a large number of literary and non-literary texts, from Chaucer's Wife of Bath to The Good Wife's Guide, from Frankenstein to poetry by Carol Ann Duffy, and from Nigella Lawson to Fast Food Nation introduces the main themes and issues students need to consider when comparing texts: themes, genre, time and place, form and structure, and intertextuality.

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In fourteen thoughtful essays this book reports and reflects on the many changes that a digital workflow brings to the world of original texts and textual scholarship, and the effect on scholarly communication practices. The spread of digital technology across philology, linguistics and literary studies suggests that text scholarship is taking on a more laboratory-like image. The ability to sort, quantify, reproduce and report text through computation would seem to facilitate the exploration of text as another type of quantitative scientific data. However, developing this potential also highlights text analysis and text interpretation as two increasingly separated sub-tasks in the study of texts. The implied dual nature of interpretation as the traditional, valued mode of scholarly text comparison, combined with an increasingly widespread reliance on digital text analysis as scientific mode of inquiry raises the question as to whether the reflexive concepts that are central to interpretation – individualism, subjectivity – are affected by the anonymised, normative assumptions implied by formal categorisations of text as digital data.

These New editions of the successful, highly-illustrated study/revision guides have been fully updated to meet the latest specification changes. Written by experienced examiners, they contain in-depth coverage of the key information plus hints, tips and guidance about how to achieve top grades in the A2 exams.

A sassy tortilla, so light she jumps off the griddle, leads an elaborate game of chase through the desert while taunting a passel of critters—two horned toads, three donkeys, four jackrabbits, five rattlesnakes, and six buckaroos. But has she met her match in Señor Coyote?

The Dangerous Art of Text Mining celebrates the bold new research now possible because of text mining: the art of counting words over time. However, this book also presents a warning: without help from the humanities, data science can distort the past and lead to perilous errors. The book opens with a rogue's gallery of errors, then tours the ground-breaking analyses that have resulted from collaborations between humanists and data scientists. Jo Guldi explores how text mining can give a glimpse of the changing history of the past - for example, how quickly Americans forgot the history of slavery. Textual data can even prove who was responsible in Congress for silencing environmentalism over recent decades. The book ends with an impassioned vision of what text mining in defence of democracy would look like, and why humanists need to be involved.

This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts. Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection. The method starts by automatically finding words that are statistically significantly more common in one subset than another, then identifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages.