Teach-in on Race 2025

How do we create new ways of looking at the world? Join award-winning data storyteller RJ Andrews as he pushes beyond the usual how-to, and takes you on an adventure into the rich art of informing.

This book provides a unique insiders account of the work of Big hART, one of Australia's leading participatory arts organisations. Founded on the values of social justice, creativity and transformation Big hART seeks to mobilise a range of community resources including young people, elders, artists, and community activists to produce high quality public performances of merit and social worth.

Storytelling in the Digital World' explores new, emerging narrative practices as they are enacted on digital platforms such as Amazon, Facebook, Twitter, and YouTube. Contributors' online ethnographies investigate a wide range of themes including the nature of processes of transformation and recontextualization of offline events into digital narratives; the effects of digital anonymity and pseudonymity on narrative practices; the strategies through which virtual communities discursively work together to solidify and negotiate their sociocultural identities; the tensions between the affordances that characterize different online media and the communicative needs of users; the structures and modes in which virtual users construct and enact participatory practices in these environments; and the significance of different spatiotemporal dimensions in the encoding, sharing and appreciation of stories.

A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics--one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever "speak for themselves." Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.