Index
Abi-Rached, J. M., 32
accelerationism, 13, 15, 41–46, 52, 81; development of, 38, 43, 44; gender politics and, 44–45; performativity and, 37
Access 4 Learning (A4L) Community, 74
accountability, 11, 23, 35, 114; data, 36, 77; education, 17, 22; personal/systemic, 17; regulatory technology of, 25; school forms of, 22; statistics and, 20
achievement, 23, 24; student test, 119; tracking, 75
actors, 3, 6, 8, 10, 58, 61, 64, 68, 85, 94, 108, 113, 133, 138; commercial, 83, 86, 89, 92; corporate, 73; human, 4, 14, 38, 41, 60; market, 16, 81, 84; policy, 24, 76, 136; public/private, 26, 93, 136, 137; technical, 63, 72, 92
Adams, P., 10
Adams, V., 142
Agamben, G., 64
agency, 40, 56, 140; algorithms and, 107; influence of, 37–38; political, 41, 142
AI. See artificial intelligence
AI Now, 141
algorithms, 2, 3, 5, 30, 32, 33, 56, 58, 69, 70, 72, 96, 105, 118, 121, 126, 128, 129, 132, 134; A-level, 131; actions and, 139; AI and, 38, 40; agency and, 107; automated, 47; bias and, 104; black boxing of, 9; capacity of, 50; challenges of, 15; contemporary, 51, 109; data and, 8–11; data sets and, 117; deep-learning, 48, 49–50, 100; education governance and, 38; explanatory, 106; impact of, 133; investigating, 66–68; machine learning and, 48, 50; misrecognition and, 112; neural network, 103; opaque, 68, 141; pattern identification and, 29; recognition of, 106; regulating, 136; uncertainty and, 50; using, 12, 22
AlphaGo, 29–30, 48, 111, 112, 113, 127; original moves of, 128
Amata, 71
Amazon Web Services (AWS), 2, 98
Amoore, Louise, 51, 52, 108–9; on algorithms, 50, 139; on automation, 107; on bias/machine learning, 103; black boxes and, 67; on digital images, 97
Amsler, S., 27
analysis, 10, 22, 26, 101, 114, 115; cluster, 29, 30; macro, 69; meta-, 49; objects in, 58; predictive, 126; speed/impact of, 126
analytics, 21, 115; action and, 119–21; AI-driven, 117; data, 8, 10, 11, 17, 60, 67, 112, 113, 116, 117, 121; learning, 10, 32; predictive, 124, 125; real-time, 32; visual, 119
Anderson, Ben, 27
Anglo-governance model, 4, 15, 33, 58, 135; development of, 18; synthetic governance and, 53
anticipation, 11–15, 17, 18, 27–31, 39; governance and, 18, 31–32, 50
Apple, 81
artificial intelligence (AI), 5, 7, 15, 27–31, 33, 35, 39, 45, 46, 55, 68, 73, 98–99, 101, 104, 127, 128, 131; acceptance of, 141–42; algorithms and, 38, 40; alternative theorization of, 37; applied, 105, 138, 141; attitudes toward, 13–14; black box of, 14, 36; common terms for techniques/technologies of, 29 (fig.); data analysis and, 32; data science and, 16, 121, 123, 126; datafication and, 53; development of, 14, 105, 106; education and, 3, 60, 67, 99, 140; education governance and, 38, 45, 52, 63, 94, 96, 135; emotive, 102; empathic, 102, 103; feminized image of, 1; Go and, 111, 112; governing practices and, 135; impact of, 56; inexplicability of, 11; instrumental reason in, 50; introduction of, 92; laboratories, 68; machine learning and, 49; presence of, 2; promotion of, 140; regulating, 13–14, 177n20; search engines and, 128; services, 98, 117; studies, 2, 48, 66, 96; using, 16, 67, 113
assessments, 28, 60, 73, 79, 94, 100, 103, 105, 106, 114, 131; formative, 73; global, 24; large-scale, 23, 62; national, 23, 92; online, 89, 90, 108; predicting, 118–19
Australian Curriculum, 89
Australian Education Senior Officials Committee, 75
Australian SIF Association (SIF AU), 75–76, 82, 90
automation, 4, 12, 14, 15, 18, 31, 38–41, 45, 50, 89, 96, 99, 101, 104, 134; AI and, 36; approaches to, 143; bias, 10, 125; cognition and, 52; collapse of, 135; conditions for, 90, 92, 94; as discrete categories, 135 (fig.); enabling, 92–94; philosophy and, 128–29; problem of, 107; rise/impact of, 13; statistics and, 34; transformations of, 47
autonomy, 24, 39, 105; degree of, 139; local, 77; school, 77
AWS. See Amazon Web Services
Bailey, P. L. J., 65
Ball, S. J., 19, 26, 61, 85–86, 128
Ballard, J. G., 17
Bartlett, L., 59
Bateson, G., 129
Baudrillard, J., 43
behavior, 7, 33, 39, 64, 96, 101, 131; influencing, 33; observed, 136
behaviorism, 12, 30, 33, 39, 101, 104
BI. See business intelligence
bias, 141, 143; algorithms and, 104; automation, 10, 125; machine learning and, 103
big data, 3, 11, 12, 39, 46, 48, 88
Bill and Melinda Gates Foundation, 73, 79, 86
biometrics, 102
black box, 36, 39, 67, 68, 105, 106, 119, 133, 143
Black, Indigenous, and People of Color (BIPOC), 104, 106, 139, 173n37
Boltanski, L., 63
Bowker, G. C., 72
boyd, d., 28
Brown, W., 21
Bulger, M., 86
Burawoy, M., 61
Burrows, R., 66
Büscher, M., 59
business intelligence (BI), 120, 127; manager, 117, 122, 123, 124, 125, 126; strategy, 60, 113, 114, 115, 116, 117, 124
calculation, 4, 17, 19, 27, 50, 80; algorithmic, 96; intensification of, 46, 52; tools of, 18
Çalişkan, K., 92
Campalo, A., 106
capitalism, 5, 43, 140; techno-, 11, 44
CCRU. See Cybernetic Culture Research Unit
Center, The, 113, 129; analytics work of, 121; BI strategy of, 114, 115, 117, 124; cognitive infrastructure and, 123; dashboards and, 119; data for, 118, 120, 126; data science and, 114–19, 123; decision-making and, 117; information strategy of, 114, 116, 118, 120; predictive capabilities of, 124; roadmap of, 115 (fig.), 125; synthetic thinking and, 127, 128
chief information officers (CIO), 75, 76, 80, 83, 84, 87, 88, 89
Class Care System, 96, 98, 99, 100, 101, 102, 103, 105, 108; behaviorism and, 104; student progress and, 106
classification, 26, 62, 97, 100, 104, 108
codes, 65, 102, 108–9; of conduct, 68
cognition, 14, 39, 46–47, 104, 138; automation and, 52; human, 2, 13, 37; machine, 13, 36, 37, 48–49, 129; nonconscious, 47, 50, 113; thinking and, 52; unconscious, 46
Colebrook, C., 57
comparison, 8, 15, 18, 25, 26, 33, 47, 136; complexities of, 59; globalized forms of, 22–23, 24; governance and, 32; international, 19, 23
computation, 10, 14, 18–20, 33–34, 106, 112; automated, 47; contemporary forms of, 17; rise of, 9; thinking and, 148n17
concepts, 33–34, 56, 57–68, 69, 70, 123
control, 36; anticipation of, 133; hybrid, 108; locus of, 133; societies of, 41
Council of Australian Governments Education Council, 75
Covid–19 pandemic, 3, 28, 81, 131, 140, 169n22
Crampton, J. W., 101
critical policy studies, 16, 18, 20, 33–34
Crogan, P., 41
Cuéllar, M.-F., 33
culture, 33, 36, 71; exteriorization of, 39; technology and, 38; thinking and, 143
Cybernetic Culture Research Unit (CCRU), 41, 43
data, 8, 48, 66, 68, 70, 77, 96, 101, 120; abstraction of, 142; administrative, 98, 132; analyzing, 1, 32, 114, 118–19; census, 104; centrism, 114; changing value and, 65; clusters, 97; collecting, 1, 25, 26, 31, 65; deluge, 55, 56; demographic, 118; digital, 12, 25, 33, 82, 100; Disneyland of, 116–17; “dummy,” 82, 84–86; education, 71, 72, 114, 172; hubs, 76, 77; knowledge extraction from, 121; labeling, 105; liquifying, 86; monetization of, 142; multidirectional flows of, 22–23; NAPLAN, 118; performance, 122, 128; processing, 1, 7, 31; quantitative, 47; standardized, 25, 73; synthetic, 85; training, 29; transitioning, 119; visual, 102; warehousing, 116. See also big data
data infrastructure, 5, 12, 15, 16, 18, 19, 46, 62, 63, 73, 89, 90, 93, 96, 97–98, 132, 134, 137; building, 66, 79, 81, 86, 92; connections by, 77, 79; cultural/political activity of, 56; digital governance and, 25–27; role of, 65; vision of, 88
data science, 10, 15, 18, 35, 67, 112, 113, 114–19, 120, 124, 125, 129, 134, 137; AI and, 16, 121, 123, 126; education, 11, 16, 21, 121, 127; experimental nature of, 121–23; introducing, 22, 126, 127; knowledge and, 22, 124; machine learning and, 15, 50; political economy of, 22; prediction in, 30–31; rationalities/prediction and, 124, 133; social good and, 32; truth and, 128; uncertainty of, 122–23
data sets, 2, 32, 48, 115, 117, 128
datafication, 3, 4, 6–7, 8, 26, 50, 70, 83; AI and, 53; impact of, 128; introduction of, 22; politics/operations of, 60
decision-making, 6, 9, 12, 14, 20, 26, 30, 48, 49, 53, 76, 99, 104, 107, 108, 113, 123, 125, 131, 144; aids to, 18; algorithmic, 51, 62, 67, 109, 142; automated, 41, 62, 175n19; Center and, 117; data-driven, 25, 27, 113; evidence-based, 116–17; inhuman functions in, 139; intentionality in, 100; interpretable, 109; organizational, 114; technological, 68
deep learning, 48, 49–50, 100, 105, 139; machine learning and, 51; research on, 44
Deleuze, Gilles, 17, 44, 55, 57, 69, 102, 127, 132; deterritorialization and, 8, 42, 43; microsociology and, 2
demography, 18, 28, 31, 117, 118
Desrosières, A., 7
determinism, 39, 63; technical, 13, 37, 99, 133, 143
dispositions, 62, 63, 92; shaping, 64, 65; theoretical, 45, 46
disruption, 3, 8, 44, 45, 70, 88
Easterling, K., 65, 136; on infrastructure, 64, 72, 93
economics, 11, 13, 19, 21, 22, 44, 92
economization of everything, 21
edtech. See education technology
education, 22, 89, 143, 144; actions, 27–28; AI and, 3, 60, 67, 140; automation in, 18, 70; data infrastructures in, 18; datafication of, 6–7; economic policy and, 13, 23; global comparisons in, 23; governing, 16, 18, 33, 69; humanist, 47, 88; mass, 11, 19, 42; national, 23–24; power in, 136; problematizing, 15; technology in, 4–6
education governance, 6–8, 10, 22, 36, 37, 50, 56, 57, 59, 67, 69, 92, 98, 103, 109, 132, 134, 137; AI and, 38, 45, 52, 63, 94, 96, 135; algorithmic, 3, 38; automation of, 96, 99; computational techniques in, 52; contemporary, 8, 33–34; contexts of, 53; control in, 40; corporate takeover of, 63; data science and, 11; deep learning in, 139; digital, 3, 7, 26; infrastructure and, 72, 73; mode of, 133; networks of, 58, 139; overview of, 15; phenomena in, 70; prediction and, 30–31; rationalities of, 2, 113; statistical reasoning in, 143; studies, 15, 35; technological change and, 128; transformation of, 17; understanding, 51, 57
education policy, 4, 6, 10, 13, 16, 17, 19, 27, 50, 58, 112, 129, 132; analysis of, 65; data science and, 127; data-driven, 73; global scales of, 24; making, 139; national, 24; studies, 15, 60
Education Services Australia (ESA), 75, 76, 89
education technology (edtech), 36, 81, 83, 87; data for, 82; meta-, 28; national, 94
Edwards, P. N., 30, 130, 131, 143
Elish, M. C., 28
Ellul, J., 5
ESA. See Education Services Australia
ethnography, network, 15, 56, 58–62
Ex Machina (film), 1–2, 7, 133, 147n2
experimentation, 33, 57, 68, 70, 129, 135
expertise, 134; computational, 137–38; governing, 18–20
exteriorization, 12, 38, 39, 41, 102, 109
extrastatecraft, 64, 93, 136, 137
Eynon, R., 28
Facer, K., 27
facial recognition, 16, 28, 96, 100–103, 106, 109, 134; biopolitical aspect of, 139; operation of, 103–4; in schools, 97–99; SIS and, 100; suspicion for, 108
Fay, B., 31
feedback, 35, 143; cybernetic, 48; formative, 121; informative, 9; loops, 12, 31–33, 52, 105, 107, 133; positive, 13, 81; real-time, 33; teacher, 141
Fenwick, T., 6
Foucault, M., 3, 18, 64–65, 69, 70
Fourcade, M., 27
Fraser Institute, 22
“Future Classroom” program, 99
futures, 11; education, 17–18; governing, 31–33
Gardiner, M. E., 45
Garland, A., 1
General Data Protection Regulation, 99, 141
Go: game of, 29–30, 111–12, 113; rules of, 174n1
Gonski, David, 89
Gordon, J., 27
Gorur, R., 59
governance, 15, 27–31, 38–41, 58, 88, 99; acceptance/authority of, 69–70; algorithmic, 39, 41, 46, 94, 100–103, 138; anticipation and, 18, 31–32, 50; automation and, 41; comparison and, 32; data-driven, 28, 56; developments in, 24, 134; digital, 18, 25–27, 137; exteriorization of, 12; human, 4, 33, 37, 96, 135, 135 (fig.), 138; infrastructure and, 63, 64, 65, 68, 92–94; machine, 4, 37, 96, 138; neoliberal, 11; network, 22–25, 76, 136, 138; pattern recognition and, 108; political, 41, 60; power/force/control and, 6; predictive, 50; rationality for, 10, 132; social/new forms of, 26; studies, 56, 68–69; technology and, 12, 35, 40, 139; thought and, 51–52; uncertainty of, 129. See also Anglo-governance model; education governance; synthetic governance
Guattari, F., 2, 44, 55, 57, 69, 102; deterritorialization and, 42, 43, 98
Hacking, I., 104
Halpern, O., 9–10
Hanwang Education/Hanwang Technology, 100
Heidegger, M., 5
Hub Integration Testing Service (HITS), 84–85
ILSAs. See international large-scale assessments
IMS Global Learning Tools Interoperability Standards, 73
information, 55, 66, 119, 124–27, 134; authenticity of, 3; contracts, 79, 79 (fig.); digital, 22, 25; dividualized, 41; educational, 108; managers, 21, 67; processing, 48; strategy, 114, 116, 118, 120; systems, 74, 87, 142
infrastructure, 5, 16, 25, 26, 47, 56, 58, 59, 69, 70, 87, 118, 136; administrative, 94; algorithmic governance and, 138; analyzing, 62, 65; building, 16, 62, 67; cognitive, 113, 123, 127, 129; conceptualizing, 62–65; education governance and, 72, 73, 137; exploration of, 15; governance and, 63, 64, 65, 68, 93–94; hub-and-spoke, 98; in-the-making, 63; infinitely expandable, 92, 137; information, 21, 66; investigating, 66–68; machine learning, 67; market-making and, 92–93; national, 73–74, 89; network, 142; studies, 12, 16, 62, 66; technical questions of, 76. See also data infrastructure
Infrastructure as a Service (IaaS), 116
innovation, 2, 8, 69, 132; democratizing, 141; methodological, 66; technological, 3, 13, 140
Institute for Higher Education Policy, 73
instrumentalism, 5, 19, 20, 138
intelligent tutoring systems (ITSs), 28
International Association for the Evaluation of Educational Achievement, 23
international large-scale assessments (ILSAs), 23, 24
interoperability, 7, 22–23, 63, 85; market integration and, 82–84; standards, 73, 86, 87
intervention, 139; behavioral, 101; informing, 103; political, 44; schooling, 119
Jones, A., 131
Jullien, F., 64
Junemann, C., 61
Kasparov, Gary, 111
Katzenbach, C., 107
Kitchen, R., 119
knowledge, 4, 30, 49–50, 67, 132; bodies of, 64; data science and, 22; digital information and, 22; governing, 20–22; policy, 127; reterritorialization of, 17; scientific, 11; technoscientific, 50
K–12 Education and Postsecondary Success, 73
Laboria Cuboniks, 44–45
Lauriault, T. P., 119
learning, 28, 48, 49, 101, 132; adaptive, 36; deep, 14; exteriorizing, 104; metrics of, 136; nonhuman, 2; reinforcement, 29, 33, 48; reward/punishment and, 30; tools, 135. See also deep learning; machine learning
Learning Services Architecture (LSA), 72, 78 (fig.), 82, 84, 89, 90; emergence of, 76–77, 79–80; NSIP and, 77
Lemke, T., 18
Levinas, E., 63
Lingard, B., 127–28
logic, 9, 22, 39, 47, 62, 81; algorithmic, 26; biopolitical, 18; computational, 48
LSA. See Learning Services Architecture
Lury, C., 55–56
Lyotard, J. F., 21, 35, 43, 48
McArdle, G., 119
McCann, E., 59
McCormick, P., 86
machine learning, 16, 28, 29, 36, 39, 104, 109, 112, 120, 121, 122, 126, 127, 139; access to, 117; AI and, 49; algorithms and, 48; approach to, 48, 53; bias and, 103; black box of, 105; data science and, 15, 50; deep learning and, 51; identifiably human in, 106; infrastructure of, 67; prediction in, 30–31; probabilistic, 33; successes in, 44; truth and, 128
machines, 52, 136; cognitive, 49; efficiency of, 48; human governance and, 135; humans and, 38, 109
McStay, A., 102
management: data, 8, 21, 77, 94; development and, 76; factory workforce, 97; public, 11; system, 135
Mangez, E., 6
Marcus, G. E., 68
market integration, interoperability and, 82–84
markets, 92–93; in Australian schooling, 80–86; creating, 84–86; standardization and, 93
Metcalf, Steve, 35
methods, 57–68, 129; inventiveness of, 55–56; rethinking, 134–35
Microsoft, 2, 74, 81, 98, 108, 116, 117, 177n27
Ministry of Public Security (China), 100
misrecognition, 103–4, 108, 112
mobility: network ethnography and, 58–62; policy, 15, 59, 60–61; sociotechnical, 60
Murphy, M., 124
National Assessment Program-Literacy and Numeracy (NAPLAN), 74–75, 118, 119, 122, 123
National Schools Interoperability Program (NSIP), 73, 79–80, 81, 84, 87, 137; data management by, 90, 94; data privacy and, 79; development of, 72; industry forum from, 83; information contracts, 79 (fig.); infrastructure, 82; interoperability and, 85, 86; LSA and, 77; mission creep and, 89, 90; OFAI and, 89, 90, 94; partnerships with, 80; public/private actors and, 93; SIF and, 74–76, 80; technical lead of, 77
neoliberalism, 21, 24, 47, 58, 153n21
Netflix, 28
Network, The, 45
networks, 6, 33, 58, 59, 62, 72; data-driven, 55; education governance and, 139; education policy, 61; global, 69; governing, 24–25; inter-organizational networks, 24; neural, 48, 49, 67, 102, 103; policy, 60–61; social, 138
nihilism: passive/active, 46; punk, 43
No Child Left Behind, 22
Noble, S. U., 106
NSIP. See National Schools Interoperability Program
OECD. See Organisation for Economic Co-operation and Development
Online Formative Assessment Initiative (OFAI), 89, 90, 92; NSIP and, 89, 90, 94; organizing “blocks” of, 91 (fig.)
optimization, 18, 115, 125, 126, 138
Organisation for Economic Co-operation and Development (OECD), 23, 24, 26, 27
Ozga, J., 6
Parisi, L., 46, 52, 126, 139, 174n51; on abductive reasoning, 123; on algorithms/machine learning, 47–48; automated thinking and, 14; on automation/philosophy, 128–29; on indeterminacy, 123; on machine cognition, 48–49
Parthenon Group, 73
pattern making, 29, 30, 96, 108–9
pattern recognition, 30, 95–96, 97, 108–9
patterns, 29, 100–103; explaining, 104–7; political, 103–4; technical, 103–4
Patton, C. V., 27
performativity, 17, 20, 35, 119, 132, 152n2; accelerationism and, 37; educational, 36; regulatory technology of, 25
philosophy, 42, 63; automation and, 128–29; empirical, 57; fieldwork in, 16, 56, 57–58, 69
PIRLS. See Progress in International Reading Literary Study
PISA. See Program for International Student Assessment
Pitcan, M., 86
Plant, S., 41
platforms, 3, 38, 73, 79, 81, 94, 98, 123, 129, 133, 141; BI, 28, 117, 127, 137; cloud, 115, 116; digital, 140, 142, 143; open source, 86; shopping, 39; updating, 89, 93
policy: changes, 122; data-driven, 28, 126; enactment, 59; initiatives, 27; learning, 59; movement of, 61; as numbers, 35; predictive, 27; prescriptive, 27; problems, 123, 127; processes, 4, 115; technology and, 60
policy-making, 27, 138; education, 16, 18, 58–59, 113; evidence-based, 21, 25; identification of, 123; understanding, 60
policy science, 10, 19–20, 21, 31, 59, 123, 132; social democratic, 20; social science and, 20; political economy, 21, 22
politics, 11, 12, 19, 20, 50, 59, 63, 69, 103, 124, 143; anticapitalist, 43; cultural, 4–6; dialogic, 13; national, 24; revolutionary, 43; synthetic, 16, 134–36, 140–42, 144. See also biopolitics
power, 4, 7, 20, 26, 36; local, 17; logistical, 65; social regulation and, 10; soft, 8; spatialities of, 61
PowerSchool, 88
prediction, 17, 18, 39, 113, 118, 133; developing, 119; education governance and, 30–31; uncertainty of, 124–27
probability theory, 29, 103, 124
problematization, 14, 46, 68–70, 94, 142–44; notion of, 56–57, 69; synthetic politics and, 134–36
Program for International Student Assessment (PISA), 23, 24
Progress in International Reading Literary Study (PIRLS), 23
public policy, 27; data-driven rationale for, 6–7
quantification, complementary systems of, 18
racism, 139
RAND Corporation, 42
rationality, 6, 30, 39, 108, 124, 132, 133, 144; algorithmic, 121, 127; cognitive, 127; data science, 112; data-driven, 4, 25–26; education, 2, 50, 113; human/machine, 133; instrumental, 10, 15, 46, 52; market, 143; network governance and, 22–25; political, 3, 4, 14, 37, 39, 65; speculative, 121
reasoning, 47, 50; abductive, 49, 123; data-based, 31; instrumental, 48; statistical, 143
reconfigurations: economic, 72; political, 72, 92; technical, 92
regulation, 45, 93; appeal and, 143; external, 141; industry-based, 141
relationality, 58; interconnectivity and, 61–62
risk, 10, 14, 31, 32, 36, 79, 108, 113, 124, 125, 129, 139, 144; calculations of, 11; existential, 48
Rizvi, F., 127–28
Roberts, B., 38
Rose, N., 32
Ross, J., 134
Ruhleder, K., 72
Savage, M., 66
Savat, D., 40
Sawicki, D. S., 27
school systems, 74, 77; data-driven, 27; recentralized, 136; schooling administrative/performance areas of, 25; digital platforms and, 140; enclosure of, 132; privatization of, 27
Schools Interoperability Framework (SIF), 73, 82, 86; development of, 83; HITS and, 85; information flows and, 77; Microsoft and, 81; NSIP and, 74–76, 80
Seaver, N., 67
Sellar, S., 59
Selwyn, N., 108
Shapiro, C., 81
Shareable Content Object Reference Model, 73
Sheller, M., 59
SIF. See Schools Interoperability Framework
SIF Association, 74
SIF AU. See Australian SIF Association
simulations, 31, 76, 118, 122, 123, 126
SIS. See student information systems
skills, 28, 36, 119; development of, 23; focusing on, 28; technical, 11, 137
social: good, 32; media, 97; practices, 26, 59; problems, 10; process, 31, 44; regulation, 10; relations, 12, 63; responsibility, 86–89
social science, 12, 34, 37, 57, 62; policy science and, 20
software, 12, 74, 83, 84, 98; development, 26, 67, 87; infrastructure and, 64; material instantiation in, 63; mathematics-tutoring, 4; medical simulation, 75
Software and Information Industry Association, 74
Spotify, 40
Sputnik, 23
standardization, 7, 62, 63, 72, 73, 81, 92, 131; development costs and, 80; enacting, 86; modern markets and, 93; normalization of, 19
State Council (China), policy initiative by, 98
State Treasury, 125
statistics, 18–20, 21, 28, 30, 104; accountability and, 20; automation and, 34; economic/educational/ employment, 23; governing education and, 19; instrumentalism of, 20
Steiner-Khamsi, G., 59
Student Information System Baseline Profile (SBP), 82
student information systems (SIS), 67, 76, 83, 94, 97–98, 108; facial recognition and, 100; SIF-compliant, 82
surveillance, 11, 29, 44, 97, 100, 104, 113, 140, 141, 142
synthetic governance, 14, 55, 69, 101, 113, 132–33; Anglo-governance and, 53; challenges by, 142; conditions of, 134; emergence of, 12–13, 15, 136; engagements with, 135; machines/bodies and, 4; material/nonmaterial support for, 72; notion of, 52–53, 108; politics of, 140, 143; possibilities of, 144; social and, 136–38; speculative futures of, 16; technological determinism and, 13; thought and, 138–41; toward, 127–29
synthetic thought, 16, 38, 46, 113, 128, 129; concept of, 36, 52, 127; theorization of, 39
technics, 39, 41, 47, 50, 88; culture and, 38; intelligent, 35; marginalization of, 52; theorization of, 38
technology, 1, 8, 9, 13, 15, 16, 38–41, 44, 51, 82, 103, 124, 141; appropriation of, 45; assimilation with, 143; automated, 96, 136; capture, 102; collective implications of, 12, 38–39; computational, 26; critical studies of, 5, 38; culture and, 4–6, 38; data-driven, 4, 87, 140, 141, 143; as dynamic system, 38; education, 137; facial recognition, 99, 102; global, 61; governance and, 12, 35, 40, 139; humans and, 36, 38; network, 93, 127; new, 81, 94; open, 90; pattern recognition, 96, 108; policy and, 60; political rationalities and, 37; problematization of, 94; regulatory, 25; science and, 17; singularity and, 144; socially progressive ends and, 140; surveillance, 97; tree-search, 112. See also education technology
technology companies/corporations, 28, 33, 44, 63, 67, 106, 140
technorationalism, 19, 20, 138
Thacker, E., 33
thought, 53, 64, 136; automated, 15, 46–50, 113, 144; cognition and, 52; coherent systems of, 3; creative potential of, 52; culture and, 143; machine cognition and, 129; synthetic aspects of, 14; synthetic governance and, 138–41
Tieto, 99
Trends in International Mathematics and Science Study (TIMSS), 23
trust, 6, 17, 18, 56, 105, 121
Turing, A., 131
uncertainty, 14, 16, 20, 32, 119, 121, 122, 132, 143, 144; algorithms and, 50; creative, 49, 50, 127, 129; modeling, 29; new norms and, 50–51; political role of, 96; of prediction, 124; tolerance for, 79
Urry, J., 59
U.S. Department of Education, 74
values, 5, 45, 65, 113, 128, 139, 143; authoritative allocation of, 7, 138; commercial, 88; cultural, 38
Varian, H., 81
Vavrus, F., 59
vision, 21, 115; machine, 97, 135
Wakeford, N., 55–56
Warburton, 71
Ward, K., 59
Watt, James, 52
Weinstein, J., 57
Weiser, Mark, 1
Wiener, Norbert, 52
Williamson, B., 28, 32, 120, 121
Witzenberger, K., 148n15