If you’ve been following the Creative and Arts-Based Methods series on MethodSpace, you have seen a lot of examples from qualitative researchers. We don’t want to neglect quantitative researchers, including those who work with Big Data. This collection of open access articles might give you some food for thought about your own research. If you have other examples to suggest, use the comment area to post links.
Entangling and Elevating Creativity and Criticality in Participatory Futuring Engagements (Balcom Raleigh & Heinonen, 2018)
This article proposes that creativity and criticality not only can but should be entangled and elevated in participatory futuring engagements. Selected concepts from creativity theory and critical futures studies are applied to develop a set of futuring games through action research. We claim that participatory processes designed to entangle and elevate creativity and criticality produce more novel and varied ideas that better fit the purposes of futures studies. This article offers four arguments for combining creativity and criticality in participatory futuring engagements. First, due to complexity and uncertainty, the future is ultimately unknowable and requires tools to probe the unknown. Second, novelty is difficult to achieve in practice while creativity and criticality can help overcome these challenges. Third, discontinuities are the main sources of futures that are most radically different from the present and will have the biggest impact. Fourth, creativity and criticality support the rigorous imagining required for exploring and discovering new possible futures. This article analyzes three experimentations in entangling and elevating creativity and criticality in game-based futuring, stemming from Causal Layered Analysis. Based on these examples, we demonstrate that creativity and criticality, when combined, help people break through the limitations of current understanding, reveal approaching tipping points, and find the “unvisited cavities” through rhizomatic knowledge creation. However, there remain challenges in evaluating how well various participatory designs support creativity and criticality in practice. Context-sensitive evaluation tools and open sharing of outcomes are needed to develop participation design principles capable of supporting creativity and criticality in participatory futuring.
Reimagining the Big Data assemblage (Carter, 2018)
Recent work on Big Data and analytics reveals a tension between analyzing the role of emerging objects and processes in existing systems and using those same objects and processes to create new and purposeful forms of action. While the field of science and technology studies has had considerable success in pursuing the former goal, as Halford and Savage argue, there is an ongoing need to discover or invent ways to “do Big Data analytics differently.” In this commentary, I suggest that attempts to produce new ways of working with Big Data and analytics might be hindered by how science and technology studies-influenced scholars have conceptualized assemblages. While these scholars have foregrounded objects’ relations within existing assemblages, new materialist philosophers draw attention to properties of objects that transcend those relations and might indicate opportunities for more creative or generative uses of Big Data and analytics.
Creative data literacy (D’Ignazio, 2017)
Working with data is an increasingly powerful way of making knowledge claims about the world. There is, however, a growing gap between those who can work effectively with data and those who cannot. Because it is state and corporate actors who possess the resources to collect, store and analyze data, individuals (e.g., citizens, community members, professionals) are more likely to be the subjects of data than to use data for civic purposes. There is a strong case to be made for cultivating data literacy for people in non-technical fields as one way of bridging this gap. Literacy, following the model of popular education proposed by Paulo Freire, requires not only the acquisition of technical skills but also the emancipation achieved through the literacy process. This article proposes the term creative data literacy to refer to the fact that non-technical learners may need pathways towards data which do not come from technical fields. Here I offer five tactics to cultivate creative data literacy for empowerment. They are grounded in my experience as a data literacy researcher, educator and software developer. Each tactic is explained and introduced with examples. I assert that working towards creative data literacy is not only the work of educators but also of data creators, data publishers, tool developers, tool and visualization designers, tutorial authors, government, community organizers and artists.
Big Web data, small focus: An ethnosemiotic approach to culturally themed selective Web archiving (Huc-Hepher, 2015)
This paper proposes a multimodal ethnosemiotic conceptual framework for culturally themed selective Web archiving, taking as a practical example the curation of the London French Special Collection (LFSC) in the UK Web Archive. Its focus on a particular ‘community’ is presented as advantageous in overcoming the sheer scale of data available on the Web; yet, it is argued that these ethnographic boundaries may be flawed if they do not map onto the collective self-perception of the London French. The approach establishes several theoretical meeting points between Pierre Bourdieu’s ethnography and Gunther Kress’s multimodal social semiotics, notably, the foregrounding of practice and the meaning-making potentialities of the everyday; the implications of language and categorisation; the interplay between (curating/researcher) subject and (curated/research) object; evolving notions of agency, authorship and audience; together with social engagement, and the archive as dynamic process and product. The curation rationale proposed stems from Bourdieu’s three-stage field analysis model, which places a strong emphasis on habitus, considered to be most accurately (re)presented through blogs, yet necessitates its contextualisation within the broader (diasporic) field(s), through institutional websites, for example, whilst advocating a reflexive awareness of the researcher/curator’s (subjective) role. This, alongside the Kressian acknowledgement of the inherent multimodality of on-line resources, lends itself convincingly to selection and valuation strategies, whilst the discussion of language, genre, authorship and audience is relevant to the potential cataloguing of Web objects. By conceptualising the culturally themed selective Web-archiving process within the ethnosemiotic framework constructed, concrete recommendations emerge regarding curation, classification and crowd-sourcing.
What are neural networks not good at? On artificial creativity (Oleinik, 2019)
This article discusses three dimensions of creativity: metaphorical thinking; social interaction; and going beyond extrapolation in predictions. An overview of applications of neural networks in these three areas is offered. It is argued that the current reliance on the apparatus of statistical regression limits the scope of possibilities for neural networks in general, and in moving towards artificial creativity in particular. Artificial creativity may require revising some foundational principles on which neural networks are currently built.
The Semantic Network Model of Creativity: Analysis of Online Social Media Data (Yu, Peng, Peng, Zheng, & Liu, 2016)
The central hypothesis of Semantic Network Model of Creativity is that creative people, who are exposed to more information that are both novel and useful, will have more interconnections between event schemas in their associations. The networks of event schemas in creative people’s minds were expected to be wider and denser than those in less creative people’s minds. Based on this theory, data from Chinese online social media, also known as “Weibo microblogging,” were analyzed. Each user’s score consisted of the metric of coverage, which represented the spread of the network, as well as the metric of density, which represented the interconnections among nodes in the network. The results showed that occupations had a significant effect on people’s creativity score. Academic scholars and writers in general had higher scores compared to other groups, such as entertainment celebrities and sport stars. The implications and limitations of this method of quantifying people’s creativity were discussed
Balcom Raleigh, N. A., & Heinonen, S. (2018). Entangling and elevating creativity and criticality in participatory futuring engagements. World Futures Review, 11(2), 141-162. doi:10.1177/1946756718807014
Carter, D. (2018). Reimagining the Big Data assemblage. Big Data & Society, 5(2), 2053951718818194. doi:10.1177/2053951718818194
D’Ignazio, C. (2017). Creative data literacy: Bridging the gap between the data-haves and data-have nots. Information Design Journal, 23(1), 6-18. doi:https://doi.org/10.1075/idj.23.1.03dig
Huc-Hepher, S. (2015). Big Web data, small focus: An ethnosemiotic approach to culturally themed selective Web archiving. Big Data & Society, 2(2), 2053951715595823. doi:10.1177/2053951715595823
Oleinik, A. (2019). What are neural networks not good at? On artificial creativity. Big Data & Society, 6(1), 2053951719839433. doi:10.1177/2053951719839433
Yu, F., Peng, T., Peng, K., Zheng, S. X., & Liu, Z. (2016). The Semantic Network Model of creativity: Analysis of online social media data. Creativity Research Journal, 28(3), 268-274.
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