28. Sociologies of Education
Paper
Searchable Students: Constituting the Knowledge Infrastructure of Educational Genomics
Ben Williamson
University of Edinburgh, United Kingdom
Presenting Author: Williamson, Ben
Research in human genetics is increasingly enacted with advanced digital technologies, with genomics methods now being introduced into international research on the biological underpinnings of educational outcomes. Molecular genomics methods using bioinformatics hardware and software are highly data-intensive and depend on complex sociotechnical infrastructures of digital technologies, scientific epistemologies and methodologies, social relations and practices, which play an active role in shaping genomic data into knowledge (Stevens 2016). In data-intensive genetic sciences, knowledge about human lives, bodies, and behaviours is produced by large-scale initiatives using biosensor equipment, laboratory computer networks, databases, automation and analytics algorithms, generating novel renderings and understandings of internally-embodied states and processes (Vermeulen 2016). In the genetic sciences, human bodies, lives, and actions are understood in ‘bioinformational’ formats, as codes, networks and programming, which are made legible with data scientific methods of searching and pattern detection (Koopman 2020).
In the last 15 years, genomic methods have been repurposed for analysing educationally-relevant biological processes and structures, particularly the ‘genetic associations’ and ‘genetic architectures’ claimed to underpin learning and achievement, and the environmental factors that mediate them (Malanchini et al 2020), by international networks of scientists in two distinctive fields. Behaviour genetics has a long history in education, including controversial involvement in intelligence testing (Panofsky 2015), and has begun using molecular genomics methods to 'discover' the biological substrates of learning, cognition and school achievement (Kovas et al 2016). Since around 2010, the new interdisciplinary synthesis known as social science genomics, or sociogenomics, has combined expertise in social statistics and bioinformatics to study the genetic bases of social and economic outcomes (Bliss 2018). Utilizing vast ‘biobanks’ of genomic bioinformation and data scientific methodologies, sociogenomics aims to ‘finally open the black box of the genome’ in order to ‘delve into the biological mechanisms and come up with a better understanding of the pathways from cells to society’ (Conley and Fletcher, 2017, 35).
A key research target of sociogenomics to date is the biological associations, architectures and mechanisms underpinning educational outcomes and the environmental factors that mediate them (Martschenko et al 2019). Scientists have generated dozens of studies and scientific articles by utilizing molecular genomics methods to analyse bioinformational samples in the millions and producing new knowledge claims about the genetic bases of educationally-relevant traits, behaviours and outcomes (Plomin 2018; Harden 2021). As such, recent research in behaviour genetics and sociogenomics signifies the emergence of ‘educational genomics’ as a domain of international research, knowledge production, and potential policy influence (Visscher 2022).
As the materialization of a ‘new biological rationality in education’ with its own distinctive methodologies and truth claims (Gulson and Baker 2018), educational genomics is an emerging science with potentially profound consequences for educational research, policy and practice internationally. This paper presents an analysis of the formation of the ‘knowledge infrastructure’ of educational genomics, drawing on an ‘infrastructure studies’ approach to datafied knowledge production (Bonde Thylstrup et al 2019). A knowledge infrastructure is a relational and sociotechnical system consisting of people and organizations, epistemologies and practices, and technologies and methods that underpin knowledge production (Edwards et al 2013). Theoretically, the paper is informed by science and technology studies conceptualizations of ‘data-centric biology’ (Leonelli, 2016) and a ‘postgenomic condition’ characterized by datafied, molecular explanations of human life (Reardon 2017). Such studies illuminate how the specific software, hardware, algorithms and data structures of bioinformatics analysis, in association with the conceptual schema of scientific communities, are reshaping how biological science is enacted and the kinds of knowledge it produces, with significant consequences in terms of biomedical explanation, public understanding, and political intervention (Chow-White and García-Sancho, 2012; Stevens 2016; Rajagopalan and Fujimura 2018).
Methodology, Methods, Research Instruments or Sources UsedSubstantively, the paper provides an examination of the social, epistemic and technical constitution of the emerging knowledge infrastructure of educational genomics. Conceptualized as an infrastructure of data-intensive biological knowledge production, educational genomics is constituted by social associations, conceptual architectures, and technical algorithms: (1) educational genomics is performed by large-scale associations or consortia, representing a ‘big biology’ mode of highly-funded, cross-sector and interdisciplinary networked knowledge production in education; (2) educational genomics enacts a specific conceptual schema, or an epistemic architecture for understanding the biological determinants of educationally-relevant outcomes and behaviours; and (3) educational genomics mobilizes methodological apparatuses powered by algorithms for data-intensive analysis of digital bioinformation and the production of new biological knowledge claims related to education.
The analysis draws on three main methods. First social network graphing software was used to identify the social relations and organizational associations that constitute educational genomics as a domain of scientific inquiry. Second, documentary analysis of a large corpus of scientific publications on educational genomics was undertaken to trace its main discourses, conceptual schema and knowledge claims. Finally, technographic descriptions of key technologies used in educational genomics were developed, focusing on key algorithmic methods including bioinformational data mining and the production of predictive ‘polygenic scores’. In sum, the methods enabled an analysis of the complex ways that an emerging infrastructure consisting of algorithmic apparatuses that are used to retrieve, process and order bioinformation, in concert with the epistemic architecture of scientists working in interdisciplinary and cross-sector associations, co-produce particular ways of understanding the biological substrates of educationally-relevant behaviours and outcomes. The results of the infrastructural formation of educational genomics are already materializing in proposals for educational policy and practice, including the use of polygenic scores to sort and categorize students by their predicted outcomes, and even to utilize genetic data in personalized forms of ‘precision education’ that would be modelled on ‘precision medicine’ in the biomedical domain.
Conclusions, Expected Outcomes or FindingsFindings of the study indicate that educational genomics generates significant implications for educational research, practice and policy. First, educational genomics signifies the emergence of ‘big biology’ as a mode of educational knowledge production, and the formation of powerful new scientific associations in education-relevant research whose large-scale quantitative research may potentially displace other infrastructures of knowledge production. It represents the entry of data-centric, international, consortium-based modes of biomedical investigation into education, directly challenging the authority of 'non-genetic' social sciences to contribute to education policy and practice.
Second, new bioinformatized conceptions of educational outcomes are produced by educational genomics. It advances the molecularization of socially-structured phenomena, assuming educational achievements are structured to a statistically significant degree by biological mechanisms that link genetic differences to brain development. Critically, and contrary to its claims of unbiased, data-scientific objectivity, educational genomics may operate politically to superimpose molecular explanations on social problems in education, privileging biological understandings while obscuring the social forces that shape educational outcomes.
Finally, by producing new bioinformatized explanations, educational genomics constructs the subjectivity of a searchable student who is quantified and known at the molecular scale through bioinformatics analysis. The searchable student is anatomized using bioinformational data mining methods as statistically significant associations between thousands of interacting genetic differences, and rendered as a single bioinformational number – a polygenic score – that predicts their educational achievement. Moreover, educational genomics significantly reconceives educational outcomes in computational terms, as the result of genetic ‘codes’, ‘programming’ and ‘information’ that are only legible through the deployment of data mining tools capable of searching for patterns in vast masses of bioinformation. As such, educational genomics produces a searchable student whose educational trajectory is said to be explainable and predictable through data mining bioinformation, and who may therefore become the subject of genetically-informed educational policy and practice interventions.
ReferencesBliss. C. (2018). Social by Nature: The promise and peril of sociogenomics. Stanford: Stanford University Press.
Bonde Thylstrup, N., Flyverbom, M. and Helles, R. 2019 Datafied knowledge production: Introduction to the special theme. Big Data and Society, July–December, 1–5. https://doi.org/10.1177/2053951719875985.
Chow-White, P.A. and García-Sancho, M. 2012. Bidirectional Shaping and Spaces of Convergence: Interactions between Biology and Computing from the First DNA Sequencers to Global Genome Databases. Science, Technology, & Human Values, 37(1), 124–164.
Conley, D. and Fletcher, J. (2017). The Genome Factor: What the social genomics revolution reveals about ourselves, our history and the future. Oxford: Princeton University Press.
Edwards, P. N. et al. 2013. Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue.
Gulson, K.N. and Baker, B. 2018. New biological rationalities in education. Discourse: Studies in the cultural politics of education, 39(2), 159-168.
Harden, K.P. (2021). The Genetic Lottery: Why DNA matters for social equality. Oxford: Princeton University Press.
Koopman, C. (2020). Coding the Self: The Infopolitics and Biopolitics of Genetic Sciences. Hastings Report, 50(3), 6-14.
Kovas, Y. et al. (2016). How genetics can help education. In Y. Kovas, S. Malykh, and D. Gaysina (eds.) Behavioural genetics for education, 1–23. London: Palgrave Macmillan.
Leonelli, S. (2016). Data-Centric Biology: A philosophical study. London: University of Chicago Press.
Malanchini, M. et al. 2020. Cognitive ability and education: How behavioural genetic research has advanced our knowledge and understanding of their association. Neuroscience and Biobehavioral Reviews 111: 229–245.
Martschenko, D., Trejo, S. and Domingue, B.W. (2019). Genetics and education: Recent developments in the context of an ugly history and an uncertain future. AERA Open, 5(1): 1-15.
Panofsky, A. 2015. What does behavioral genetics offer for improving education? Hastings Center Report, 45(5), S43–S49.
Plomin, R. 2018. Blueprint: How DNA makes us who we are. London: Allan Lane.
Rajagopalan, R.M. and Fujimura, J.H. 2018. Variations on a Chip: Technologies of Difference in Human Genetics Research. Journal of the History of Biology, 51, 841–873.
Reardon, J. (2017). The Postgenomic Condition: Ethics, justice, and knowledge after the genome. Chicago: University of Chicago Press.
Stevens, H. (2016). Hadooping the genome: The impact of big data tools on biology. BioSocieties, 11, 352–371.
Vermeulen, N. (2016) Big Biology. N.T.M. 24, 195–223.
Visscher, P. (2022). Genetics of cognitive performance, education and learning: from research to policy? npj Science of Learning, 7, https://doi.org/10.1038/s41539-022-00124-z
28. Sociologies of Education
Paper
Blinding Data: Exploring Data Epistemologies in School
Cathy Hills
University of Edinburgh, United Kingdom
Presenting Author: Hills, Cathy
New kinds of data-driven activities carried out in school configure people, practices and pedagogies. “Datafication”, or the transformation of the social and natural world into machine-readable format (Williamson, Bayne and Shay, 2020), signifies how complex digital systems that sort, order and classify, are routinely used to predict and channel behaviours in ever-more intensive and opaque ways. The impact of datafication in the schools sector, however, has largely been examined in speculative terms; this paper offers a detailed ethnographic analysis of how data are enacted in schools, focusing on the formation of new data epistemologies of schooling.
Internationally, datafication has intensified the amount of pupil information collected, modelled and analysed by a plethora of technologies supporting teaching, learning and school administration. The government of education in systems around Europe and beyond has become a system of governance in which non-state organisations and commercial actors have gained agency in decisions over the purpose and direction of a public domain under market conditions (Ozga, 2009).
The marketisation of education via quantification is considered to have changed both pedagogies and people (Ball, 2003). A focus on attainment, targets and comparative metrics occasioned the rise of “teaching to the test” and a concentration of instructional effort on students at examination pass borderlines (Hardy and Lewis, 2017). With burgeoning amounts of data to grapple with, teachers are now encouraged, not simply to become data-literate, but to translate the “profoundly emotional and human process” (Castañeda and Williamson, 2021) of education into a series of calculative operations (Grant, 2022; Selwyn et. al., 2021).
A focus on metrics is significant for the meaning and quality of education as it teaches a “hidden curriculum” (Mertala, 2020) that coveys a reductive episteme of knowing by numbers. Jarke and Breiter assert that,
The education sector is one of the most noticeable domains affected by datafication, because it transforms not only the ways in which teaching and learning are organised but also the ways in which future generations (will) construct reality with and through data. (2019, p.1)
To examine the formation of new data epistemologies in the secondary school sector, this paper is informed by sociomaterial theory and post-qualitative methodology (Orlikowski, 2007, Lather and St Pierre, 2013). Sociomaterial or relational ontologies emphasise the performative nature of measurement that works to constitute the phenomena it purports to represent. Such an appreciation means that no neutral position of exteriority exists from which to observe, analyse and report on things, including the research itself. Instead, the observer, the observed and the means of observation are combined in constitutive relations that collapse the distinction between ontology and epistemology and call for rethinking humanist binaries and traditional research boundaries.
Post-qualitative sensibilities are cultivated because they trouble notions of a privileged human subject and data as neutral and straightforwardly representative. They recognise the fallibility and partiality of all knowledge accounts and attempt to work productively with uncertainty and multiplicity. The post-qualitative is intent on unpicking the “epistemic codes” underpinning traditional qualitative research and all knowledge regimes “which posit ‘truth about’ and ‘power over’” (Taylor, 2017, p.313) and is apposite for research into epistemic claims for education.
Methodology, Methods, Research Instruments or Sources UsedPost-qualitative inquiry has been called an uncomfortable social science (Taylor, 2017), not least for urging that research “begins with an encounter with the real, not with method” (St Pierre, 2019, p.11). My encounter with the real took the form of a case study in one, unexceptional, English secondary school over the course of several months in order to experience how data were actually done on the ground.
I used ethnographic means of inquiry that included semi-structured interviews, observations, and “scavenging” techniques (Seaver, 2017, p.7). The latter entailed maintaining a presence in school in the hope of participating in ad-hoc conversations and fortuitous happenings. Post-qualitative inquiry proposes the rethinking of these activities so that ethnography is less about entering the subjective lifeworlds of individuals, and more about exploring what relations, including my own, come together to produce the research and the phenomenon under study.
The ethnographic approach responded to calls to “interview objects” (Adams and Thompson, 2011) by enlisting material and technical phenomena as research participants. The imbrication of the social and technical as a “sociotechnical assemblage” (Williamson and Perrotta, 2018) required being open to the agency of material items and attentive to human interactions with technical systems, noting the latter’s invitational design which privileges certain operations over others. Decuypere’s “walkthrough methods” enabling “the unfolding of highly intricate details” (2021, p.76) of technical systems was drawn upon. The process reveals the intentions of platform designers by focusing on implied usage and was instructive for understanding “how platform interfaces configure specific types of users … in highly determined ways” by a coded “grammar of action” (ibid, p.76). Being alert to idiosyncratic system use was productive for apprehending how users and technologies “enfold and unfold in each other” (ibid, p.76/7) in the enactment of data practices.
Conclusions, Expected Outcomes or FindingsIt is difficult to draw definitional boundaries around data activities as they pervade much of what happens at school. However, the post-qualitative ethnographic examination has surfaced a range of sociomaterial ways in which routine practices such as attendance and assessment are being affected by data activities and epistemologies.
Attendance monitoring was far from a straightforward audit of who was in or out of school. The practice was a melee of people, programs and policies, interwoven with elements of care and control. The school’s conscientious attempts to produce accurate attendance numbers entailed the recruitment of students into the registration assemblage. Pupils were enlisted as runners chasing anomalous attendance marks across school in dataflow subroutines.
Exploring the school’s assessment practices, book stickers emerged as potent material-discursive devices. Sticky labels displaying a neat grid of pupil data were adhered to the front of every pupil exercise book. Following the social life of the measures on these mini material data dashboards revealed the underlying structures of thought that earned them a place in school. Stickers were both unpeeling and holding fast: the data they circulated in inexpensive, analogue form were imprecise and often resisted, but the data epistemology that held them in place – one that found convenience in the school’s average performance for ease of comparison – was considered to remain as long as the school was judged by its numbers.
The analysis has revealed how a powerful data epistemology is enacted in complex sociomaterial practices. Beyond the immediate context of the study, this indicates how datafied schooling in Europe and beyond benefits from ethnographic study in order to understand its pervasive and subtle effects in everyday practice.
ReferencesAdams, C. A. and Thompson, T. L. (2011) ‘Interviewing objects: including educational technologies as qualitative research participants’, International Journal of Qualitative Studies in Education, 24(6), pp. 733–750. https://doi.org/10.1080/09518398.2010.529849
Ball, S. (2003) ‘The teacher’s soul and the terrors of performativity’, Journal of Education Policy, 18(2) pp. 215-228. https://doi.org/10.1080/0268093022000043065
Castañeda, L. and Williamson, B. (2021) ‘Assembling New Toolboxes of Methods and Theories for Innovative Critical Research on Educational Technology’, Journal of New Approaches in Educational Research, 10(1), pp. 1-14. https://doi.org/10.7821/naer.2021.1.703
Decuypere, M. (2021) ‘The Topologies of Data Practices: A Methodological Introduction’, Journal of New Approaches in Educational Research, 10(1), pp. 67–84. https://doi.org/10.7821/naer.2021.1.650
Grant, L. (2022) ‘Reconfiguring Education Through Data: How Data Practices Reconfigure Teacher Professionalism and Curriculum’ In: Hepp, A., Jarke, J., Kramp, L. (Eds) New Perspectives in Critical Data Studies. Transforming Communications – Studies in Cross-Media Research. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-96180-0_10
Hardy, I. and Lewis, S. (2017) ‘The “doublethink” of data: educational performativity and the field of schooling practices’, British Journal of Sociology of Education, 38(5), pp. 671–685. https://doi.org/10.1080/01425692.2016.1150155
Jarke, J. and Breiter, A. (2019) ‘Editorial: the datafication of education’, Learning, Media and Technology, 44(1), pp. 1–6. https://doi.org/10.1080/17439884.2019.1573833
Lather, P. and St. Pierre, E. A. (2013) ‘Post-qualitative Research’, International Journal of Qualitative Studies in Education, 26(6), pp. 629-633. https://doi.org/10.1080/09518398.2013.788752
Mertala, P. (2020) ‘Data (il)literacy: education as a hidden curriculum of the datafication of education’, Journal of Media Literacy Education, 12(3), pp. 30-42. https://doi.org/10.23860/JMLE-2020-12-3-4
Perrotta, C. and Williamson, B. (2018) ‘The social life of Learning Analytics: cluster analysis and the ‘performance’ of algorithmic education’, Learning, Media and Technology, 43(1), pp. 3-16, https://doi.org/10.1080/17439884.2016.1182927
Seaver, N. (2017) ‘Algorithms as culture: Some tactics for the ethnography of algorithmic systems’, Big Data and Society, 4(2), pp. 1-12 https://doi.org/10.1177/2053951717738104
Selwyn, N., Pangrazio, L. & Cumbo, B. (2021) ‘Knowing the Datafied Student: The Production of the Student Subject through School Data’, British Journal of Educational Studies, 70(3) pp. 345-361, https://doi.org/10.1080/00071005.2021.1925085
St Pierre, E. A. (2019) ‘Post Qualitative Inquiry in an Ontology of Immanence’, Qualitative Inquiry, 25 (1), pp. 1-16. https://doi.org/10.1177/1077800418772634
Taylor, C. A. (2017) ‘Rethinking the empirical in higher education: post-qualitative inquiry as a less comfortable social science’, International Journal of Research and Method in Education, 40(3), pp. 311–324. https://doi.org/10.1080/1743727X.2016.1256984
Williamson, B., Bayne, S. and Shay, S. (2020) ‘The datafication of teaching in Higher Education: critical issues and perspectives’, Teaching in Higher Education, (25)4, pp. 350 – 365. https://doi.org/10.1080/13562517.2020.1748811
28. Sociologies of Education
Paper
Turning Power / Resistance Upside-Down to Critically Affirming Digital Educational Leadership
Danilo Taglietti
University of Naples - Federico II, Italy
Presenting Author: Taglietti, Danilo
Despite little agreement on how to conceptualize resistance in Critical Education and Leadership Studies (CEPaLS), its working is generally recognized as a ‘struggle against/with’ something, which is practically treated as coming before. It works through opposition: an act or a ‘counter-conduct’ that is opposed to a previously settled gesture, directive or policy. This way of intending ‘resistance’ has contributed to substantial accumulation of knowledge about individuals or collectives who challenge a dominant form of power.
The same understanding of power/resistance finds rich literature in the Critical Studies of Digital Education, where powerful digital platforms are often presented as effecting processes of subjugation of school subjects, among which school leaders. Many studies, in this frame, produces knowledge about the strategies and tactics the latter adopts for subtly coping or overtly struggling against/with the former.
This is a critique that is based on taking a distance from the world to exactly discern what is bad and what is good: bad policies and the good, rare counter-conduct, bad platforms and the good heroic opting-out school. Following Latour, this way of judging the world in completely negative terms has ‘run out of steam’ and has become useless and not so different by conspiracy theories: it promotes a sort of flat discredit about how the world is going on that gives no justice to the multiple, varied and differentiated realities that live in everyday practices. This is even more relevant when talking about the educational world, so crucial for the possibility of pedagogically countering the effects of the current complex scenario, marked by the same negativity and deconstructivist tendencies that animate the negative critique: CEPaLS do not simply talk about educational leadership, but performatively construct it. An additional plane is crossed when coming to the digitalization of educational leadership because it is a process intertwined with the capitalist acceleration in the educational world. So, what we need ‘is to associate the word criticism with a whole set of new positive metaphors, gestures, attitudes, knee-jerk reactions, habits of thoughts’, in order to perform alternative educational possibilities.
In this presentation, we will explore the positive and affirmative consequences of considering resistance as something that ‘is in some way before what it resists’ when critically researching digital educational leadership. In a neo-materialist and vital frame, we use Deleuze’s re-reading of Foucault’s concepts of knowledge as ‘two-fold’ and power as ‘a diagram’ to turn upside-down the power/resistance conceptual couple. By this reversal, we adopt a resistance/power perspective: resistance emerges as the multiple ways in which things are going on in the world before and despite the attempts of normalization promoted by institutions through codifying knowledges.
By using data produced for a qualitative study on the introduction and the impact of the digital governance of education in Italy, we show that resistant leadership is a widespread practice emerging as a situated and contingent assemblage of the human, the digital, and the analogic, whose daily effort in leading a school is repeatedly challenged by digitalization policies.
Methodology, Methods, Research Instruments or Sources UsedThis presentation builds on theory to develop a set of analytical lenses, which use is at last exemplified by using ethnographic materials from ongoing field researches.
Theoretically, we introduce the re-reading that Deleuze advances of two Foucauldian concepts: the diagram of power and the knowledge. The Diagram is a crucial concept, stated that it is what permits Foucault to establish the identical dispositivity of prisons, factories, schools, and hospitals. Starting from this, Deleuze tries to go to the roots of the concept and looks at it as a transposition of the Nietzschean Will to Power, differential and genetic at the same time. We can think of the concept of diagram as ‘a function that must be detached from any specific use’: an attempt to organize the whole social life following a specific rule; an ‘abstract machine’ that incites the social dispositif towards ‘educate, look after, punish, and so on’. But the diagram is a sort of a two-faced Janus: the perfect structuration of the social whole is the aim it tends, without ever reaching it. As well as the ordering of what can happen, it is also an ‘emission of singularities’ that moves away from its attempt of normalization. Acting as an immanent cause coextensive with the social field, it is implied also in the production of the unforeseen.
But how is this difference produced? Deleuze points out that, in Foucault, everything is knowledge (ontology is an epistemology), but knowledge itself is two-fold. The discursive and the non-discursive play an equal role: the visible and the articulable, the expression and the content, are the two forms of exteriority, put together by the Diagram of power. In the exteriority and irreducibility between the articulable and the visible that compose knowledge, there is the possibility for the diagram to fail its normalization and for singularities to emerge. For the otherness, the different, the diverse, the varied to be alive.
More: these singularities are what the diagram, without stopping to emit, wants to normalize. In this way, the upside-down is completed: singularities are in some way before power, and resistance ‘is in some way before what it resists’.
Starting from this, we will sketch out a possible articulation of analytical lenses that deploy the resistance/power perspective along four dimensions useful for being applied to educational field research: the when, the what, the who, and the where of the resistance functioning.
Conclusions, Expected Outcomes or FindingsBy exemplifying the use of our analytical lenses in a real school life scene from ongoing ethnographic research on the digitalization of school leadership in Southern Italy, we show that the enactment of a digitalization policy could be easily considered as a no-resistance case, from a power/resistance perspective; while a more nuanced and complex understanding could be presented through the application of a resistance/power frame.
We argue that our analytical lenses help in framing differently the present of educational resistance. Our exemplification helps us make clear that, in that specific school, connected to that specific digitalization policy enactment, what was at stake was the epistemic space of education: how those subjects consider their educational roles, identities and values. Their resisting communitarian set of organizational practices is challenged by the new articulation of technologies and ideas related to the effectiveness of school management. The headteacher, who could seem to lead the digitalization policy process, is differently lightened: despite all, she is still there, at the entrance hall, every morning, looking at the eyes of her pupils and teachers, but in a different assemblage with technologies and ideas. An opportunity for the survival and prosecution of resistance is produced through this kind of political (re-)presentation: certainly not an oppositional resistance, but rather a ‘mangling’ one.
We argue that this understanding of resistance connects CEPaLS and critical post-humanities, producing knowledge with and giving visibility to the ‘missing’ resistant leadership which has not yet been subject of knowledge but is deeply involved in the political production of other educational possibilities. It allows: (a) to compose post-human subjects through the alliance of digital, analogic and human entities; (b) to give value to under-valued daily endeavours of making ‘minor’ education(s) (still) possible; and (c) to accelerate the production of non-capitalist ‘modes of becoming’.
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