The affirmative’s drive to counter Chinese technology kills international relations with China and compromises data innovation.

On November 14, 2022, during the G20 summit, President Biden said to President Xi Jinping there ‘need not be a new Cold War’ between the US and China. The ongoing geopolitics in which we situate our analysis involves the bi-directional US-China geopolitical tensions over ideologies and technology and economic leaderships, fueled by the global COVID-19 epidemiological crisis and surging anti-China sentiments (Sanger, 2021). As Biden’s latest remark makes clear, on both sides many have described the current geopolitical climate as the ‘New Cold War,’ despite that today’s context differs drastically from the Cold War of the last century. The invocation of the ‘Cold War’ imagery, however, is telling in that it frames the US–China antagonism as one between two purportedly opposite politico-economic systems – liberal democracy and capitalism on one side, and authoritarianism/totalitarianism and socialism/communism on the other – each advancing their own version of modernity, development, and progress (Dai, 2021; Brands and Gaddis, 2022).3 That China has incompatible national goals and values with the US is a bipartisan consensus in the US, despite domestic political polarization (Hirsh, 2022).Behind anxieties over ideological divides lie deeper concerns over military, economic and technological leadership. The China-US trade war has escalated since 2018. In the latest, ‘tech arms race,’ American media jump to stories about China outstripping the US and Europe in AI products and scientific publications (Li et al., 2021). The discourse revolves around the legitimacy of innovation and national security concerns. According to NATO, China is the ‘global security threat.’4 The US government has increased institutional surveillance over ethnically Chinese or China-related researchers. Under the ‘China Initiative’ (dropped in March 2022 but with lingering repercussions), numerous scholars were arrested on the false charge of ‘IP thefts’ or ‘scientific espionage’ (Subbaraman, 2021). Recently, through the CHIPS Act – which commentators identify as the ‘cold-war era strategy’ – Biden administration tightened restrictions on exchanges and trade of semiconductor technologies, including prohibiting US citizens and residents working for chip plants in China (Mihm, 2022; Herman, 2022). Meanwhile, blaming the US for escalating tensions, China launched ‘Global Security Initiative’ to promote its own vision of global order and partnership (Eguegu, 2022). China’s surveillance industry has been expanding globally at a stunning rate (Huang & Tsai, 2022).This antagonism in the ‘tech arms race’ is preoccupied with national data infrastructures, regarded as the game changer (Liu, 2021). The US and many other countries banned Huawei’s 5G technology, while also accusing China of ‘exporting digital authoritarianism’ to the West and Africa via its ‘surveillance technologies,’ including FinTech, facial recognition technologies, and platforms such as TikTok (Edel & Shullman, 2021). Meanwhile, Chinese tech companies strive to attract transnational venture capitalists by boasting their exceeding wealth of data (e.g., Lee, 2018). Indeed, Chinese data-driven enterprise has benefitted from structural reasons conducive to data accumulation: an enormous population, lax market regulations, the relative absence of public organizing about data privacy, and long-term infrastructural investments from the state (Wu, 2020). This preoccupation with data infrastructures is almost allegorical for us to think about how regulated sanctions and exclusions of datafication are constitutive of knowledge enterprises generically. The COVID-19 pandemic, while fueling global anti-Asian hate crimes, has further deteriorated China’s already-intense international relations. From Trumpian racism to the later ‘lab-leak theory,’ the rhetoric of blame frames China as a biotechnological threat, extending a longer history of an orientalist imagination of ‘the East’ in relation to ‘contagion’ and ‘contamination’ (Wald, 2008). Age-old Orientalism has also converging with a relatively recent exoticization of Asia as a hypermodern alien existence looming over the West (Siu & Chun, 2020). Meanwhile, both governments seek to enlist already monopolistic AI tech companies for public health management. Beijing, in particular, has been calling upon Alibaba, Tencent, and Baidu to utilize their existing technological setup and data science experiments to track populations in the name of national security and citizen care (Kim et al., 2021). The persistent weaponization of data infrastructures, which is integral to the ‘tech arms race,’ further focuses societal attention to the pandemic accumulation of digital data. Meanwhile, COVID-19 has posed severe, but unevenly distributed, material restrictions over China research, scholarly and journalist alike. On top of the increased risks of international border-crossing, China, adhering to a ‘zero-COVID’ policy after reigning in the initial outbreak in a stunning manner, has closed in onto itself with draconian measures. With rare exceptions, obtaining Chinese visas has become a daunting task for non-citizen researchers. Foreign journalists from major press outlets such as New York Times were expelled since the outbreak (Munroe et al., 2020). Those with the access must overcome strenuous and unpredictable testing and quarantine requirements, skyrocketing costs, and extraordinary time commitment to make their way in. These constraints amount to limited physical access to fieldwork, informants, and archives. This level of limitation, some argue, is comparable to what previous generations of Western social scientists experienced prior to China’s reform and ‘opening up’ (Millwood, 2022). Amid the rhetoric of the ‘New Cold War,’ we should recall that, during the (actual) Cold War, US social sciences underwent significant restructuring, incentivized to serve war agendas against the East Bloc (Gilman, 2016). It is also instructive to note that, during the same period, the US also saw an outpouring of government funds and private philanthropy to support ‘area studies,’ aimed to gather intelligence and develop so-called ‘indigenous knowledge’ of foreign territories, including China. Such support has been dwindling in the post-Cold War era (Feng et al., 2021). As academic jobs have become fast-paced and competitive, graduate mentors in the social sciences discourage students from spending time on foreign language study and fieldwork (Stevens et al., 2018). In short, as the anglophone social sciences are caught up in the ongoing geopolitical crisis and embedded in its incentive structures, various types of contextual, embodied knowledge about non-EuroAmerican areas are being diluted, warped, and marginalized. Adding to this shift away from ‘older’ methods is the expansion of ‘computational social science’ and the embrace of STEM talent without social science training. This forms an extraordinary academic ecology when it comes to China, which the broader anglophone discourse projects as an antagonistic and othered state (Siu & Chun, 2020). The conditioning effect is particularly salient in Media and Communication Studies and Information Sciences, which, compared to other social science disciplines, lack the tradition of localized area-specific research (Chakravartty et al., 2018). For example, in this literature, China’s political system is typically both the by-default condition for, and the significance of, the empirical study. This research design formula reflects what Guan (2019) calls ‘authoritarian determinism,’ which assumes that the ‘authoritarian apparatus determines the fundamental logic and operational mechanisms of China’s communication sphere’ (p. 739). As embodied, contextual understandings of Chinese society decrease both in proportion and in legitimacy, this enduring power dynamic in the West/US-centric academia gets accentuated, making China-related accounts extra-conducive to the imperatives of the Cold War mentality. Communication Studies and Information Studies gravitate towards digital media and technologies as both the sites and means of research (see, Hargittai & Sandvig, 2015). These fields’ growing research interest in digital surveillance, reflective of societal concerns, also plays a role. China has become a free-floating signifier for the omnipotent digital dystopia, without history, culture, or institutional complexity. In its extreme rendering, China is equated with (the equally acontextual) totalitarian Nazism or Stalinism, defined by an all-crushing state – the ‘Big Brother’ – that controls every aspect of citizens’ life and mind; all this hides in the dark that requires heroism to infiltrate (Li & Nardi, 2021). This genre proliferates especially around China’s Social Credit System, despite scant evidence for empirical analysis (e.g., Zuboff, 2018). Many are fixated on ‘thought experiment,’ aiming to deduce a dystopian alternative to mobilize protection of the liberal West’s purported integrity. These writings exhibit what Roh et al. (2015) call ‘technoorientalism,’ where China is a technical myth, where Chinese technologies, the Chinese society, and the Chinese state are conflated into one monolith. This amounts to a striking case where blackboxing a political and racial Other, and blackboxing nefarious new technologies, converge. We should note that in academic outputs, there is a mirroring, but equally simplified, image of China without ‘authoritarian determinism’ or its relevant features. Here, Chinese digital data are posited as ready proxies for studying generic social constructs. Such constructs, like ‘public sphere’ and ‘agenda-setting,’ are usually developed from examining phenomena in Western contexts and presume particular institutional conditions that China lacks. In Information Studies, more particularly, Chinese users are often categorized under the generic umbrella of ‘non-Western users,’ whose data supposedly offers design implications for better ‘support’ abstracted user needs. This treatment is not dissimilar to computational social science research within the PRC, which tends to stay away from ‘politically sensitive topics’ and concentrates on narrow, so-called ‘application’ problems (Stockmann, 2018). Importantly, what this China shares with the above ‘techno-orientalist’ China is its ‘digital simulacrum’ quality, devoid of local textures and multi-layered power relations (also see Wu, 2020). Broader geopolitical forces and particular academic environments have both conditioned the enactment of ‘China as a blackbox’ that centers on an open/close dichotomy. We begin with questioning the ‘openness’ of platform data and its deployment to ‘disclose’ China.In the past decade, the proliferation of datasets amassed from Chinese platforms, such as Weibo and WeChat, has served as a window via which China is seen and made sense of. Since COVID-19, restrictions over scholars’ physical mobility have further nudged them toward digital data, while restrictions over ordinary people’s physical mobility boost their virtual activities in digital environments – the production of such data (see Hargittai, 2022). To be clear, using platform trace data to study human conduct is a global trend (Wu & Taneja, 2020). Such data, which continue to flood our world in novel forms of granularity, tend to be seen as ‘unobtrusive’ recordings of our activities and behaviors. In recent proposals to develop computational social science, much effort is concentrated on expanding data collaborations and data infrastructures with platform companies (Lazer et al., 2020; Wu et al., 2020). Admittedly, there has been major pushback that addresses issues of representativeness, privacy, and precarious access controlled by platforms (for a brief review, see Wu & Taneja, 2020). Besides these concerns, more pertinent to our intervention are what Wu and Taneja (2020) call the ‘measurement regimes’ of platform trace data, by which they mean the structural arrangements via which platform data materializes into its final form. Similarly drawing on STS perspectives, their analysis historicizes the measurement of user behavior across media and markets to show that it is always designed and executed to serve a given institutional context. As its latest regime, platform datafication is also not a ‘neutral’ process, Rather, the authors demonstrate that platform data are generated as platforms’ records of their own behavioral experimentation, calibrated to answer questions germane to these companies. Simply put, a platform sets up its measurement infrastructure to create data that ‘improve’ design features, fuel advertising analytics, and please investors. In truth, platform governance boils down to the platform’s numerous strategies for managing data on user behavior, as well as utilizing such data to influence user behavior. In any society, platform datafication also operates under broader structural and infrastructural forces. Indeed, Chinese platform governance involves the commercial platforms’ ever-shifting architectures and modulations. But additionally, it also involves the platforms’ often hidden attempts to fulfill political directives from government agencies through content curation and removal – what Guobin Yang (2022, p. 36) calls ‘party-sponsored platformization.’ For example, Weibo’s Trending list, which researchers often cite to indicate the center of public attention, is constantly doctored. On Xinjiang policies or Russia’s war on Ukraine, for another example, can we deduce Chinese popular sentiment from social media expressions? A Chinese user deep in the weeds knows how relentless the platforms have been downplaying voices dissenting to the official line. This is what we must confront: academic researchers who acquire platform datasets seldom have such information about platform governance. This is unsurprising, as this information may breach platforms’ business secrets, compromise political control, and is especially removed from the orbit of quantitative researchers operating from afar.5 As a result, as researchers draw conclusions from this data – which is not necessarily fungible from one moment to the next – they are also drawing on a historical set of conditions and decisions made by the tech platforms. Consider such topics as dynamics of ‘diffusion of ideas,’ influencer economy, and digital activism, etc. What researchers identify and interpret as correlations within platform trace data thus amount to partial and contorted accounts of human conduct, which essentially conceal platform interventions – rankings, recommendations, display aesthetics, and so forth, including content moderation under mutating rules and censorship directives. Moreover, when platform data serves as the only data source, their intrinsic features also limit knowledge production. Platform trace data and data scraped from online venues are typically about the behavior of individual user accounts or the semantic content of sprawling textual fields. Lacking direct measures for gender, income, ethnicity, and so forth, these data – despite their abundance – are ‘unfriendly’ to the investigation of structural inequality (Wu, 2020). In short, since platform design and interventions follow the logics of both commerce and state governance, without thoughtful measures to countervail their constitutive roles in platform trace data, repurposing this data will reproduce what these powers uphold as the normative façade of China. Notably, all the above limitations are not restricted to quantitative studies. Likewise, as we may imagine, our caution against the negligence of institutionalized – yet often opaque – processes of datafication applies to research relying on other forms of digital data that avail itself as ‘open source’ – a term doing rhetorical work by pr esenting data as ‘transparent’ and ‘available to all.’ The question is: How can we pursue and present accounts approximating ‘truth,’ if we rely on data that is being obliquely manipulated and managed? This is exactly the conundrum of blackboxing China: The attempts to open the black box of China effectively lead us to another black box, that of Chinese platform governance via digital systems. Rethinking this sociotechnically is the way out.