Abstract
We live in times of great stirring turbulence, both natural and human-made. The role of research and academia in the context of such proximal global challenges is both critical and simultaneously, subject to criticism and public scrutiny. Where competition once dominated, cross- and interdisciplinary collaboration is increasingly seen as the route to competitive edge. The imperative to identify and delineate the social and public impact of research, specifically funded research, is now a fundamental expectation of all funding agencies.
In this context, clearly defined and tangible objectives, metrics and end user outcomes are used as key measures of success by funding councils. Furthermore, in an environment of volatile and unpredictable funding streams, and unprecedented technological advances, funding for research is more readily accessible for those who are willing and able to facilitate successful interdisciplinary and industry collaborations. What I have termed incentivized inclusiveness (Freshwater, 2014). In addition, researchers who are willing to recognize the relative merits and limitation of a variety of approaches, what we might term focused diversity, to achieve the same ends and to appreciate the coexistence of multiple truths have an opportunity to flourish and to lead movement (Freshwater, 2012). It could be argued that both ‘incentivized inclusiveness and focused diversity are essential components of contemporary mixed methods research (MMR).
Researchers, particularly those attached to academic institutions, are also expected to determine, create, innovate, inform and indeed underpin the very nature of research itself, that is, research methods, approaches, theories, concepts and paradigms. Indeed, the Journal of Mixed Methods Research is firmly situated in this space, attempting as it does to expand the boundaries of the dialogue around MMR, or as Hesse-Biber and Johnson (2013) frame it, continuing to “coming at things differently” (p. 103).
However, in a climate within which counting and formulaic approaches to research have been and continue to some extent to hold the position of a dominant discourse, it is still worth reflecting on the partial view that such machine consciousness and algorithmic thinking perpetuates. And in doing so, to come at things differently. Let us imagine, for example, an algorithm that looks something like:
or
It could be argued that this algorithm is the formula of triangulation. Indulge me further for a moment: Algorithms and machine consciousness are of course very useful where similar repeatable outcomes are desired consistently and constantly. Algorithms are ideal for formulaic, robotic, disembodied, and objective modes of defining, categorizing, predicting, and boxing certain types of research questions. But as we know, this is only one lens through which to view a problem and it is not necessarily inclusive in the way in which mixed methods identifies itself. Moreover, algorithmic thinking around problems and grand challenges can lead to constrained solutions and small worldviews. People are not formulas; neither are they numbers. We did not come in boxes; neither do our ideas. However, we can soon get boxed in by our ideas, which we then flat pack, sell them off to other people, who very often do not realize that we ourselves felt hemmed in by both the ideas we flat packed and the conceptual flat pack itself (Freshwater, 2014).
How might we think about this in the context of MMR? As authors, reviewers, researchers, practitioners who employ and enjoy the beauty of pluralistic kaleidoscopes, how can we ensure that we too, continue to apply a critical evaluative lens to our tendencies toward formulaic and algorithmic thinking around the MMR paradigm. MMR is arguably an inclusive approach to knowledge generation and legitimization. Indeed, some might opine that MMR is a leader in inclusive approaches to research. Nevertheless, it is all too easy, in our valiant endeavor to support an inclusive and welcoming paradigm for all forms of knowledge generation and human experience, which many of us value deeply, to find ourselves using a mechanistic approach to assessing, evaluating, and reading MMR. In other words, applying algorithmic formulas to assess and categorize creativity and uncertainty.
Fisher and Freshwater (2014), using the work of moral philosopher Charles Taylor, suggest that we might care deeply for our own values, while continuously (and relentlessly) subjecting them to scrutiny and interrogation. In other words, taking a reflexive stance, that applies both a rational, objective, technical and emotional, subjective, professional lens to the topic of inquiry and indeed the methodological frame. Therein, providing an opportunity for an outcome that is both truly relevant to, and inclusive of, the experiences of society and end users and is built on clearly defined tangible outcomes.
Using this feminist understanding of aesthetic rationality and personalized ethics, I question how embodied forms of personalized ethics can be reconciled with the growing use of technological expertise, this in the context of our cyborgic desires (Freshwater, 2014). Similar questions have been posed by theorists, such as Locsin (2013), whose theory of technological competency as caring contends that technology not only completes human beings but also, as importantly, enhances the ability of human beings to harmoniously deliver ethical and high-quality care in relationships. In research, this demands an integrated approach, not simply in conducting research but also to understanding of how a research question comes about. Moreover, it is crucial to have a deliberative and purposeful awareness of the nature and type of thinking that underpins a research theory and indeed how that same thinking might both legitimize and critically evaluate the concepts in question, without necessarily pushing us into the flat pack box of our own formulaic thinking.
What then is the solution to explicating an inclusive approach to knowledge generation and MMR? Even asking the question in this way makes clear my own human frailty and desire not only to find an answer but also to have a way of formulating that in an articulate and measureable way. Well, maybe the answer is not another algorithm! But perhaps, as Locsin (2013) and I have noted, the human condition leans toward a preference for something concrete and stable (albeit illusory), by which to measure outcomes and quality, and capture our life experience. So, I might propose, at the risk of generating yet another formula, Aesthetic rationality as a lens through which to view integration and triangulation of research methods and data, for knowledge generation that is both universally translatable and particularly meaningful.
Coming at the algorithm differently:
