In SF analysis, I discovered that out of 55 papers and chapters that were selected for in- depth examination only three included an approach that clearly filtered specific genre examples, to varying degrees of scale. Of these, only two applied quantitative methods. Aimee Slaughter examined a very small number of early pulp fiction magazines (from Amazing for 1939 and Astounding for 1934) to draw conclusions about cultural interests in the “new world” of radiation in the early twentieth century (Slaughter, 2014). This study applied close reading to the texts. The two studies that applied quantitative methods to a number of sources were the paper by Bina et. al. that examined 64 novels and movies from the period 1815-2016 to examine how they might provide insight into human interests and fears about science and innovation that could be used to inform government policy (Bina et. al., 2016). Perhaps the first work to describe a method of analysing larger volumes of text in some depth, however, was Eric Rabkin’s paper demonstrating the work of the Genre Evolution Project (GEP) between 1998 and 2004, which has carried out analysis on 1,959 short stories (Rabkin, 2004) to discover
55 potential for including material that situated the stories in context (advertising, editorials et.al.) but the GEP database only records the content of stories.
Considering the demonstrable impact of research applications of SF content and concepts, the small number of quantitative methodologies found in the review is significant, as is the variable quality of their implementation. William Bainbridge (2004) provides a cogent and well-structured methodological example of the potential for SF to be an insightful and meaningful tool, and employs quantitative methodologies to analyse chaotic and granular data of the sort that is found in cultural studies. The strength of Bainbridge’s multidisciplinary research in semantics is evident in the consistent quality of his methodology. Bainbridge argues that technological progress in development of quantitative tools for analysis enables a convergence between the Arts and Sciences, which was previously not possible. Rabkin’s study included the intent to remove qualitative selection bias by engaging a pool of researchers from the field of literature to apply content categories to stories so that they could then be analysed quantitatively. Yet, predefinition of coding categories appears to have constrained the freedom of analysis, and this may have resulted from not collaborating with academics from other disciplines more familiar with statistical sampling and coding methods. For example, a strong correlation between the categories of genre form “alien contact” and genre content “alien” was one of the “provocative results” (466) described in Rabkin’s findings, but such a correlation is naturally predicated at a one-to-one ratio in SF narratives. The study findings would arguably have been strengthened by the identification of the statistical insignificance of this correlation by members of a multidisciplinary research team. Bina et al. applied iterations of subjective criteria to identify films and novels that were analysed mathematically to create new suppositions, but the methodology is not described in replicable detail, and would have been subject to confirmation bias arising from the use of pre-selected online databases to validate the choices made (as I will discuss further in “Defining the Genre,” below, as it adds to the argument for a clear definition of SF).
Employing quantitative tools to analyse research findings in fields that have traditionally been dominated by qualitative methods is constrained by access to
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of the author’s own specialism may improve the application of quantitative
methodology to humanities research. The application of techniques such as data and text mining, robust statistical and structured methodological analysis, to studies that are traditionally located in the humanities would support the convergence between the arts and sciences, and the breaking down of the perceived gap between them that was highlighted by Snow in 1961. For analysis of literary content to become a credible tool in broader research contexts, a stronger focus on the use of quantitative, replicable, methodologies is to be recommended. The focus and mechanism of the current study is a direct response to this recommendation, which led to the inclusion of
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DEFINING THE GENRE
Menadue, C. B., Giselsson, K., & Guez, D. (2018). An Empirical Revision of the Definition of SF: It’s All in the Techne. [In Review: SAGE Open].
To examine the public perspective on SF, and how it resonates with them, it is necessary to determine exactly what the public are imagining when they use the term “science fiction.” The responses to the survey data gathered from two online surveys (Menadue, 2016b, 2017a) made it apparent that the popular definition of SF does not correspond well with existing academic definitions of the genre. For the purpose of academic research this might be considered a curiosity, as we know that the popular view is rarely the critical view. However in the case of research that relies upon popular perspectives, values and beliefs to generate insights that are intended to be relevant to public interests, and even drive public policy, it is essential to consider how, exactly, this most popular of genres is viewed by the public and to contrast this with the academic interpretations. Otherwise there is the risk of coming to conclusions that are intended to speak for the majority of the public, but are in reality extrapolated from the differing perspective of an academic minority, and of correspondingly limited general relevance.
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