“An important feature underlying many algorithms is predictability: They are not only used to work our problems; they know the problem in advance and are tasked to achieve a specified result. [...] The work of administrating algorithms (e.g., what we do to solve the problems that we care about) requires biocentric methods and methodologies that can only produce dehumanizing mathematical results. [...] What comes into clear view, then, is not simply the racist result but the administrative and methodological steps that require racism before they begin to work through and toward the problem.” (McKittrick, 2021, pp. 109-111)

Educational research methodologies are increasingly shaped by algorithmic reasoning - quantitative models,
standardized metrics and data-driven systems that influence how educational phenomena are understood, measured, and enacted. From standardized testing to biometric assessments, algorithms and socio-technological systems appear as neutral tools for advancing knowledge. The myth of ‘science as method’ has entrenched the idea that scientific knowledge is distinct from everyday knowledge (Biesta et al., 2024). While scientific methods are made after the event (Latour, 1988) - the clarity about what counts as knowledge or how technology works is made after the event whereas at the moment when such knowledge and technology is still in the making, algorithmic reasoning(s) predict the future, knowing “the problem in advance” and “tasked to achieve a specific result” (McKittrick, 2021, p. 109). However, such methodologies are embedded in histories of colonialism, racism, and imperialism (Andrews & Castillo, 2016; Noble, 2018; Smith, 2012), often operating as technologies of control and exclusion.

If we have anyone to thank for the distinction between qualitative and quantitative research, it would be literary historian Mary Poovey (1998). She traces the emergence of the modern fact to the double-entry accounting system to seventeenth-century Italy. Of this new accounting practice used to keep track of arriving products in ports, she argues that the logic used to generate such a system altered the epistemological landscape–one in which understanding things and numbers trumped qualitative narratives as the preference for what got considered to be a “fact.” It is this empirical penchant for “accounting” for objects, things and numbers to inhibit and disincentivize theft, that drove the emergence of what constitutes the “modern fact.” In a similar manner, Ian Hacking (1990) argues in his historical studies that statistical processes and procedures emerged in an effort to tame chance. Once the Copernican Revolution occurred, scientists, inventors, and thinkers understood that humans exist in a state of continual risk, probability and chance (Hacking, 1990). Trying to understand and tame such risk and chance became their mission. Hacking (1990) maintains that a crucial aspect of Enlightenment philosophy of science was to manage and control variables that obstructed a priori structures and essential forms of knowledge. The emergence of probability, according to Hacking (1975), occurs through a series of intellectual accidents that have constrained thinking to this day. Gould (1996) questions the “myth that science itself is an objective enterprise” noting that “science must be understood as a social phenomenon” (p. 53). It is no mystery that scientific thinking has constraints, but to show how historical factors impinge on current thinking compels one to wonder about how certain preceding conceptions of science restrict the current uses of research practices. What has become evident from these and other historical studies of the emergence of science as an epistemological practice was that everyday concerns and demands necessitated other ways of knowing and doing.

More current commentators on science attempt to further understand the distinctions among different aspects of science. Anthony Giddens (1976) theorizes that understanding in the “natural sciences” (e.g. physics, biology, botany) occurs through causal explanation from the outside (erklären) whereas in the social sciences inquiry is based on understanding (verstehen) humanity through an empathetic identification with the other as an attempt to grasp their subjective experiences. In brief, Giddens argues that this distinction between the natural and social sciences can’t rely on the same style of explanation. For example, electrons in motion cannot be used for human actions, which have appeal to beliefs, desires, and goals. In order to understand human actions, a different style of explanation, such as an intentional stance, is required. Furthermore, Howe (1992) argues that such an intentional stance excludes the natural sciences approach altogether. He contends that in this way the natural sciences have come to be identified with positivism and the intentional stance with interpretivism (Le Grange, 2018, p. 2). Thomas Kuhn (1962), furthermore, theorizes that science modifies and changes paradigms only when deviations within the methods change often through accidents.

In educational research, scholars have also argued along similar lines (Berliner, 2002; Greene, 2007). These scholars have argued quite convincingly that research in the social sciences remains inconclusive due to the many factors associated with designing research in contexts (Berliner, 2002; Greene, 2007). For example, David Berliner (2002) maintains that educational research, unlike research in the “hard sciences” (e.g. physics) is the most difficult science of all because in the social world humans exist within multiple interlocking social networks that make conducting research, especially objective positivistic research, rather difficult. He also illustrates that, due to changes in social conditions, seemingly conclusive research findings from one decade can be completely outdated in another. In other words, the foundations of social science continually shift and adjust as social conditions with its multiple networks change. Furthermore, Berliner argues that conceptions of “science” in educational researchers have relied on positivist and post-positivist epistemologies to legitimize their findings and knowledge foundation and production in the field rather than on epistemologies of doubt and skepticism that were used by early purveyors of scientific thinking. To evoke Giddens, causal results, connections and relationships remain impossible in social science, and thus educational research.

Scholars in the field of qualitative research methodologies balk at the term science and instead use the word
“research” in their description of the field (Creswell & Ploth, 2024). In fact, in many of the textbooks in qualitative research the word “science” does not even appear; or, when it does appear, it becomes the object of critique (St. Pierre, 2002). Instead, scholars rely on other terms such as perspectives, research, philosophical assumptions, inquiry and interpretive frameworks, to name a few. Perhaps rather than relying on historical understanding of science as “doubt” or as research as inherently constituted (Popkewitz, 2020), scholars in the field have refrained from defining science and how to operationalize it.

This special issue aims to interrogate how “science” works as algorithmic reasoning(s) in educational research methodologies, taming or predicting what we do as educational researchers to explore the problems that we care about. We seek to explore the politics of “science” in educational research methodologies in terms of governing practices, which involves the ironies of doing science. It is ironic in that if the algorithmic reasoning(s) are successful, they only serve the person’s interests in which their desires are also shaped by the algorithmic reasoning(s) that they are already part of. If those algorithms perpetuate the historical conditions that are racialized, colonialized, and imperialized, as McKittrick (2021) argues, do we want to follow or be “outside” of algorithmic reasoning (Pleasants et al., 2023; Wynter, 2001); and, even if this is possible (or not), particularly in educational research? For this special issue, we invite authors to engage Katherine McKittrick’s (2021) work on critical interrogation of science, algorithmic reasoning(s), and livingness in (un)doing educational research and its methodologies with the following questions.

Questions for Authors to Consider:
1. How can new conceptions of science drawn from McKittrick’s book assist educational researchers in
reimagining politics, everyday life and “livingness”?
2. What key concepts (e.g. Black diaspora, a black sense of place, black livingness, playlists, poetry,
storytelling, method-making, citation, relationality, algorithm of resistance, radical interdisciplinarity)
become reimagined and counter to algorithmic notions and politics of science and scientificity and
“uncomfortable” relationalities to doing educational research?
3. In what ways does McKittrick’s book help scholars to think more deeply about the relationship between
historically marginalized populations (e.g. Black, Queer, Asian, Disabled, Indigenous, Latinx) and research
methodologies in educational research?
4. How does McKittrick’s book help scholars reconsider taken-for-granted practices and conceptions of
research methodologies in educational research?
5. How might scholars practice non-biocentric “humanizing” research based on McKittrick’s and Wynter’s
conceptions of the human?
6. What happens when algorithmic discourses and practices crack, fracture, or fail?
7. How can scholars resist algorithmic discourses and practices via research methods and methodologies or in other ways?

We are looking for experimental writing that engages with McKittrick’s book, Dear Science, and Other Stories (2021), and ones that address one or two of the above questions.

References
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Berliner, D. C. (2002). Comment: educational research: the hardest science of all. Educational Researcher, 31(8), 18-20. https://doi.org/10.3102/0013189X031008018

Biesta, G., Takayama, K., Kettle, M., & Heimans, S. (2024). How ‘academic’ should academic writing be? Or: why form should follow function. Asia-Pacific Journal of Teacher Education, 52(2), 121–125.
https://doi.org/10.1080/1359866X.2024.2324582

Creswell, J. W., & Poth, C. N. (2024). Qualitative inquiry and research design: Choosing among five approaches (5th ed.). SAGE Publications, Inc.

Gould, S. J. (1996). The mismeasure of man (revised and expanded edition). W. W. Norton & Company.

Greene, J. C. (2007). Mixed methods in social inquiry. Wiley.

Hacking, I. (1990). The taming of chance. Cambridge.

Hacking, I. (2006). The emergence of probability: A philosophical study of early ideas about probability, induction and statistical inference (2nd ed.). Cambridge University Press.

Howe, K. R. (1992). Getting over the quantitative-qualitative debate. American journal of education, 100(2), 236-256. https://doi.org/10.1086/444015

Kuhn, T. (1962). The structure of scientific revolutions. University of Chicago Press.

Latour, B. (1988). Science in action: How to follow scientists and engineers through society. Harvard University Press.

Le Grange, L. (2018). What is (post) qualitative research?. South African Journal of Higher Education, 32(5), 1-14. https://doi.org/10.20853/32-5-3161

McKittrick, K. (2021). Dear science and other stories. Duke University Press.

Noble. S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

Pleasants, J., Krutka, D. G., & Nichols, T. P. (2023). What relationships do we want with technology? Toward
technoskepticism in schools. Harvard Educational Review, 93(4), 486–515. https://doi.org/10.17763/1943-
5045-93.4.486

Poovey, M. (1998). The history of the modern fact: problems of knowledge in the sciences of wealth and society. University of Chicago Press.

Popkewitz, T. S. (2020). The impracticality of practical research: A history of contemporary sciences of change that conserve. University of Michigan Press.

Smith, L. T. (2012). Decolonizing methodologies: Research and indigenous peoples (2nd ed.). Zed books.
St. Pierre, E. A. (2002). Comment: “Science” rejects postmodernism. Educational Researcher, 31(8), 25-27.
https://doi.org/10.3102/0013189X031008025

Wynter, S. (2001). Towards the sociogenic principle: Fanon, identity, the puzzle of conscious experience, and what it is like to be “Black.” In A. Gomez-Moriana & M. Duran-Cogan (Eds.), National identities and sociopolitical changes in Latin America (pp. 30–66). Routledge.