'The Technological Fix as Social Cure-All: Origins and Implications' by Sean F Johnston in (2018) 37(1) IEEE Technology and Society Magazine 47-54 comments
In 1966, a well-connected engineer posed a provocative question: will technology solve all our social problems? He seemed to imply that it would, and soon. Even more contentiously, he hinted that engineers could eventually supplant social scientists - and perhaps even policy-makers, lawmakers, and religious leaders - as the best trouble-shooters and problem-solvers for society [1]. The engineer was the Director of Tennessee's Oak Ridge National Laboratory, Dr. Alvin Weinberg. As an active networker, essayist, and contributor to government committees on science and technology, he reached wide audiences over the following four decades. Weinberg did not invent the idea of technology as a cure-all, but he gave it a memorable name: the “technological fix.” This article unwraps his package, identifies the origins of its claims and assumptions, and explores the implications for present-day technologists and society. I will argue that, despite its radical tone, Weinberg's message echoed and clarified the views of predecessors and contemporaries, and the expectations of growing audiences. His proselytizing embedded the idea in modern culture as an enduring and seldom-questioned article of faith: technological innovation could confidently resolve any social issue. ...
Weinberg did not invent the idea of technology as a cure-all, but he gave it a memorable name: the “technological fix.” This article unwraps his package, identifies the origins of its claims and assumptions, and explores the implications for present-day technologists and society. I will argue that, despite its radical tone, Weinberg’s message echoed and clarified the views of predecessors and contemporaries, and the expectations of growing audiences. His proselytizing embedded the idea in modern culture as an enduring and seldom-questioned article of faith: technological innovation could confidently resolve any social issue.
Weinberg’s rhetorical question was a call-to-arms for engineers, technologists, and designers, particularly those who saw themselves as having a responsibility to improve society and human welfare. It was also aimed at institutions, offering goals and methods for government think-tanks and motivating corporate mission-statements (e.g., [3]).
The notion of the technological fix also proved to be a good fit to consumer culture. Our attraction to technological solutions to improve daily life is a key feature of contemporary lifestyles. This allure carries with it a constellation of other beliefs and values, such as confidence in reliable innovation and progress, trust in the impact and effectiveness of new technologies, and reliance on technical experts as general problem-solvers.
This faith can nevertheless be myopic. It may, for example, discourage adequate assessment of side-effects — both technical and social — and close examination of political and ethical implications of engineering solutions. Societal confidence in technological problem-solving consequently deserves critical and balanced attention.
Adoption of technological approaches to solve social, political and cultural problems has been a longstanding human strategy, but is a particular feature of modern culture. The context of rapid innovation has generated widespread appreciation of the potential of technologies to improve modern life and society. The resonances in modern culture can be discerned in the ways that popular media depicted the future, and in how contemporary problems have increasingly been framed and addressed in narrow technological terms.
While the notion of the technological fix is straightforward to explain, tracing its circulation in culture is more difficult. One way to track the currency of a concept is via phrase-usage statistics. The invention and popularity of new terms can reveal new topics and discourse. The Google N-Gram Viewer is a useful tool that analyzes a large range of published texts to determine frequency of usage over time for several languages and dialects [4], [5].
In American English, the phrase technological fix emerges during the 1960s and proves more enduring and popular than the less precise term technical fix.
We can track this across languages. In German, the term technological fix has had limited usage as an untranslated English import, and is much less common than the generic phrase technische Lösung (“technical solution”), which gained ground from the 1840s. In French, too, there is no direct equivalent, but the phrase solution technique broadly parallels German and English usage over a similar time period. And in British English, the terms technological fix and technical fix appear at about the same time as American usage, but grow more slowly in popularity. Usage thus hints that there are distinct cultural contexts and meanings for these seemingly similar terms. Its varying currency suggests that the term technological fix became a cultural export popularized by Alvin Weinberg’s writings on the topic, but related to earlier discourse about technology-inspired solutions to human problems.
Such data suggest rising precision in writing about technology as a generic solution-provider, particularly after the Second World War. But while the modern popularization and consolidation of the more specific notion of the “technological fix” can be traced substantially to the writings of Alvin Weinberg, the idea was promoted earlier in more radical form.
In 'Automating Learning Situations in EdTech: Techno-Commercial Logic of Assetisation' by Morten Hansen and Janja Komljenovic in (2023) 5 Postdigital Science and Education 100–116 the authors comment
Critical scholarship has already shown how automation processes may be problematic, for example, by reproducing social inequalities instead of removing them or requiring intense labour from education institutions’ staff instead of easing the workload. Despite these critiques, automated interventions in education are expanding fast and often with limited scrutiny of the technological and commercial specificities of such processes. We build on existing debates by asking: does automation of learning situations contribute to assetisation processes in EdTech, and if so, how? Drawing on document analysis and interviews with EdTech companies’ employees, we argue that automated interventions make assetisation possible. We trace their techno-commercial logic by analysing how learning situations are made tangible by constructing digital objects, and how they are automated through specific computational interventions. We identify three assetisation processes: First, the alienation of digital objects from students and staff deepens the companies’ control of digital services offering automated learning interventions. Second, engagement fetishism—i.e., treating engagement as both the goal and means of automated learning situations—valorises particular forms of automation. And finally, techno-deterministic beliefs drive investment and policy into identified forms of automation, making higher education and EdTech constituents act ‘as if’ the automation of learning is feasible.
Education technology (EdTech) companies are breathing new life into an old idea: education progress through automation (Watters 2021). EdTech companies are interested in portraying these processes as complex and bringing significant value to the learner and her educational institution, even when actual practices do not always reflect such imaginaries (Selwyn 2022). For example, EdTech companies may claim that artificial intelligence (AI) is a key part of their product, when in fact, actual computations are much simpler. It is therefore vital to disentangle EdTech companies’ imagined and actual automation practices.
We propose the concept of ‘automated learning situations’ to disentangle automation imaginaries from actual practice. ‘Learning situations’ are the relationships between students, teachers, and learning artefacts in educational contexts. ‘Automated’ learning situations refer to automated interventions in one or more of these relationships. In practice, EdTech companies automate learning situations by capturing student actions on digital platforms, such as clicks, which they then use for computational intervention. For example, an EdTech platform may programmatically capture how a student engages with digital texts before computing various engagement scores or ‘nudges’ in order to affect her future behaviour.
It is useful to conceptualise such automation as techno-material relations mapped along two dimensions: digital objects and computing approaches. While current literature on EdTech platforms has already uncovered how platformisation reconfigures pedagogical autonomy, educational governance, infrastructural control, multisided markets, and much more (e.g. Kerssens and Van Dijck 2022; Napier and Orrick 2022; Nichols and Garcia 2022; Williamson et al. 2022), the two dimensions bring more conceptual clarity to the technological possibilities and limitations of actually existing automation practices. Furthermore, they allow us to unpack techno-commercial relationships between emergent automation and assetisation processes.
EdTech is embedded in the broader digital economy, which is increasingly rentier (Christophers 2020). This means that there is a move from creating value via production and selling commodities in the market, to extracting value through the control of access to assets (Mazzucato 2019). Assetisation is the process of turning things into assets (Muniesa et al. 2017). Depending on the situation, different things and processes can be assetised in different ways (Birch and Muniesa 2020). This includes taking products and services previously treated as commodities—something that can be owned through purchase and consequently fully controlled—and transforming them into something that can only be accessed through payment without change in ownership (Christophers 2020). A useful example is accessing textbooks in a digital form by paying a subscription to a provider such as Pearson +, instead of purchasing and owning physical book copies. Assetising a medium of delivery changes the implications for the user. For example, when customers buy a book, they own the material object but not the intellectual property (IP) rights. With the ownership of the book itself, i.e., the physical object, comes a measure of control: they can read the textbook as many times and whenever they want, write in the book, highlight passages, sell it to someone else, use it for some other purpose entirely, or even destroy it. On the contrary, paying a fee for accessing the electronic book via a platform transforms how users can engage with the content because the platform owner holds the control and follow-through rights (cf. Birch 2018): they decide when books are added and removed, what users can do with the book and for how long, and—crucially—what happens to associated user data. Generating revenue from a thing while maintaining ownership, control, and follow-through rights is an indication that this thing has been turned into an asset for its owner. We, therefore, ask: does the automation of learning situations contribute to assetisation processes in EdTech, and if so, how?
In what follows, we first present our conceptual and methodological approach. We then unpack the digital objects used to construct learning situations. Next, we discuss how interventions are automated differently depending on computing temporalities and complexities. We conclude by discussing three assetisation processes identified in the automation of learning situations: the alienation of digital objects from students and staff, the fetishisation of engagement, and techno-deterministic beliefs leading to acting ‘as if’ automation is feasible.
