What does it meme? The exegesis as valorisation and validation of creative arts research

This paper will draw on Richard Dawkin's idea of the 'meme' to discuss how the creative arts exegesis can operate as valorisation and validation of creative arts research. According to Dawkins, the rate and fecundity of replication permits an artefact to achieve recognition and stability as a meme within a culture. The value and application of traditional forms of research is underpinned by a secondary order of production, publication, that establishes visibility of the work and articulates its empirical processes and findings as sources of social benefit and cultural enhancement.

In the arts, conventional modes of valorisation such as the gallery system, reviews and criticism focus on the artistic product and hence, lack sustained engagement with the creative processes as models of research. Such engagement is necessary to articulate and validate studio practices as modes of enquiry.

A crucial question to initiate this engagement is: 'What did the studio process reveal that could not have been revealed by any other mode of enquiry?'

Re-versioning of the studio process and its significant moments through the exegesis locates the work within the broader field of practice and theory. It is also part of the replication process that establishes the creative arts as a stable research discipline, able to withstand peer and wider assessment. The exegesis is a primary means of realising creative arts research as 'meme'.

Continuous validation for data analytics systems

From a future history of 2025: Continuous development is common for build/test (continuous integration) and operations (devOps). This trend continues through the lifecycle, into what we call `devUsage': continuous usage validation. In addition to ensuring systems meet user needs, organisations continuously validate their legal and ethical use. The rise of end-user programming and multi-sided platforms exacerbate validation challenges. A separate trend isthe specialisation of software engineering for technical domains, including data analytics. This domain has specific validation challenges. We must validate the accuracy of sta-tistical models, but also whether they have illegal or unethical biases. Usage needs addressed by machine learning are sometimes not speci able in the traditional sense, and statistical models are often `black boxes'. We describe future research to investigate solutions to these devUsage challenges for data analytics systems. We will adapt risk management and governance frameworks previously used for soft-ware product qualities, use social network communities for input from aligned stakeholder groups, and perform cross-validation using autonomic experimentation, cyber-physical data streams, and online discursive feedback.