Article on “Engaged research-led teaching: composing collective inquiry with digital methods and data”

A new article on “Engaged research-led teaching: composing collective inquiry with digital methods and data” co-authored by Jonathan GrayLiliana BounegruRichard RogersTommaso VenturiniDonato RicciAxel MeunierMichele MauriSabine NiedererNatalia Sánchez-QuerubínMarc TutersLucy Kimbell and Anders Kristian Munk has just been published in Digital Culture & Education.

The article is available here, and the abstract is as follows:

This article examines the organisation of collaborative digital methods and data projects in the context of engaged research-led teaching in the humanities. Drawing on interviews, field notes, projects and practices from across eight research groups associated with the Public Data Lab (publicdatalab.org), it provides considerations for those interested in undertaking such projects, organised around four areas: composing (1) problems and questions; (2) collectives of inquiry; (3) learning devices and infrastructures; and (4) vernacular, boundary and experimental outputs. Informed by constructivist approaches to learning and pragmatist approaches to collective inquiry, these considerations aim to support teaching and learning through digital projects which surface and reflect on the questions, problems, formats, data, methods, materials and means through which they are produced.

Make a deal with Gephisto

Mathieu Jacomy and Anders Munk, TANT Lab & Public Data Lab

6 minutes read

Make a deal with Gephisto

Gephisto is Gephi in one click. You give it network data, and it gives you a visualization. No settings. No skills needed. The dream! With a twist.

Gephisto produces visualizations such as the one above. It exists as a website, and you can just try it below. It includes test networks, you don’t even need one. Do it! Try it, and come back here. Then we talk about it.

https://jacomyma.github.io/gephisto/

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“Algorithm Trouble” entry in A New AI Lexicon

 A short piece on “Algorithm Trouble” for AI Now Institute‘s A New AI Lexicon, written by Axel Meunier (Goldsmiths, University of London), Jonathan Gray (King’s College London) and Donato Ricci (médialab, Sciences Po, Paris). The full piece is available here, and here’s an excerpt:

“For decades, social researchers have argued that there is much to be learned when things go wrong.¹ In this essay, we explore what can be learned about algorithms when things do not go as anticipated, and propose the concept of algorithm trouble to capture how everyday encounters with artificial intelligence might manifest, at interfaces with users, as unexpected, failing, or wrong events. The word trouble designates a problem, but also a state of confusion and distress. We see algorithm troubles as failures, computer errors, “bugs,” but also as unsettling events that may elicit, or even provoke, other perspectives on what it means to live with algorithms — including through different ways in which these troubles are experienced, as sources of suffering, injustice, humour, or aesthetic experimentation (Meunier et al., 2019). In mapping how problems are produced, the expression algorithm trouble calls attention to what is involved in algorithms beyond computational processes. It carries an affective charge that calls upon the necessity to care about relations with technology, and not only to fix them (Bellacasa, 2017).”

Investigating infodemic – researchers, students and journalists work together to explore the online circulation of COVID-19 misinformation and conspiracies

Over the past year researchers and students at institutions associated with the Public Data Lab have contributed to a series of collaborative digital investigations into the online circulation of COVID-19 misinformation and conspiracies.

Researchers and students contributed to a series of “engaged research led teaching” projects developed with journalists, media organisations and non-governmental organisations around the world.

These were undertaken in association with the Arts and Humanities Research Council funded project Infodemic: Combatting COVID-19 Conspiracy Theories, which explores how digital methods grounded in social and cultural research may facilitate understanding of WHO has described as an “infodemic” of misleading, fabricated, conspiratorial and other problematic material related to the COVID-19 pandemic.

These projects led to and contributed to a number of stories, investigations and publications including:

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