“The more observers learn about scientists and their livelihoods, the more we come to appreciate the sheer diversity of their activities, the vast compass of their societal locations, and the multitude of ways their findings have become stabilized and accredited as knowledge. What keeps this daunting multiplicity from defeating analysis is the dominance of certain identifiable institutional structures involved in organizing scientific inquiry in the modern period. Scientists have never subsisted as a purely autarkic self- organized discourse community, contrary to the rhetoric dominant during the Cold War era. Rather, they have always been enmeshed in complicated alliances with and exclusions from some of the dominant institutions of our era: primarily, the commercial corporation, the state, and the university.” (Mirowski 2011).

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Although science has traditionally lurked in the shadows of headline news, scientists have found their field thrust into the limelight with increasing regularity in recent years. 

A cursory glance at the home page of the Financial Times recently warned of the consequences of the Trump administration’s ongoing funding cutbacks, both in terms of research output and national reputation; the rise of the anti-vax movement as symptomatic of a dwindling faith in science, and Google’s new AI co-pilot, tasked with bootstrapping biomedical research by overseeing the creative burden of generating research programmes. Absent a working knowledge of the history and economics of science, one might be forgiven for diagnosing the current moment as one of exception, a moment where a reckless new presidential administration is running roughshod across an otherwise stable and vital infrastructure. As with so many other political crises however, Trump is merely an apotheosis - a local optimum - of processes that were set in motion and cultivated over several decades. To attempt to clear a path forward, I want to provide a brief history of the changing economics of science in recent decades. Once the heterogeneous nature of science as a social process - as opposed to some centuries old monolith - is established, we can start thinking about what a truly democratised science might look like, and - crucially - whether it is feasible to build the political power to bring such a vision into existence.

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The changing face of science

I will preface this section by noting the considerable debt I owe to the works of Philip Mirowski. His understanding and articulation of the shifting sands of the economics of science have greatly informed my own understanding of this subject. It is true that the future of science in the US looks less certain than in recent memory, but the assault on basic research and the shift toward the commercialization of science has a long and well-documented history, for those who care to seek it out. Indeed, the commercialization of science tracks well with the advent of neoliberal political hegemony. World War 2 and the Cold War were the backdrops against which the state was heavily involved in generating, organizing and funding vast scientific research programmes, be it on the vast scale of the Manhattan project, or the production of the ICBM. In most cases, the military was a centrally important player in these projects. This was not unique to the United States. For example, the choice to locate CERN - founded in the early ‘50s - in Geneva was largely driven by the need to impose a pan-European physics laboratory on the German physics community in the aftermath of WW2 (Krige, 2006). In such an environment, grand scientific narratives were easy to buy into, both for those within the scientific community, and for the general public. 

A number of significant events shifted the structure of research funding. Although we tend to look to the end of the Cold War, the commercialization of science and the shift toward private funding sources began much earlier than with the dissolution of the Soviet Union in 1992. Indeed, as Slaughter and Rhoades point out, the rhetoric of globalization seeped into the scientific agenda with the advent of neoliberalism:

“The ‘competitiveness’ agenda was proposed as a basis for science and technology policy in the 1980s, during the Reagan and Bush administrations, and found an articulate and ardent champion in President Clinton. Science and technology policy directed toward competitiveness uses government funds to commercialize science and technology via corporations and R&D agencies. The aim is to increase U.S. shares of global markets and to increase the numbers of high-technology, high-salaried jobs in the domestic economy. With the breakdown of the traditional epics - ‘winning the cold war’, ‘the fight against disease’ - that justify spending on science and technology, the rhetoric of ‘global competitiveness’ is an effort to create a new narrative of heroic proportion that serves similar purposes.” (Slaughter and Rhoades 1996).

Although the 1980 Bayh-Dole Act has garnered the most attention, it was but one piece of a raft of legislation that ushered in this new era in science. The Bayh-Dole act enabled universities and businesses to own the patents to discoveries that were made via government funding. One can already see in this act the incentivisation to shift away from basic research - that which focuses on the generation and refinement of theory and prediction - toward the potentially far more lucrative realms of applied research (concerned with developing marketable technology or protocols). We also see an increase in private funding of scientific research, relative to federal government funding, a process that ramps up in the late 1970s, as depicted in Figure 1, taken from Mirowski (2011).  

Figure 1: Decline in federal government R&D funding relative to industry, 1953-2006.

At this point it might be tempting to lay the blame for these changes in the funding landscape at the hands of a small number of political actors. However this would be akin to losing oneself amongst the foliage of political programmes, and missing the topography of political economy in its entirety. Indeed, the shift toward the commercialization of science and the focus on enforcement of IP, was a consequence of the long economic downturn, the beginning of which Robert Brenner locates in 1973:

“Far from achieving unparalleled prosperity thanks to the freeing up of capital and commodity markets that has taken place over the last quarter century, the advanced economies have performed decreasingly well since the end of the long postwar boom around 1973, and their performance during the years from 2001 to 2007 was the worst for any comparable period in the last half a century.” (Brenner 2010).

The end of the post-war boom ushered in an era of declining profit rates, with little incentive for the capitalist to invest in industrial production, given the falling rate of return. Here we find the driver of the shift toward financialization and the commercialization of science. However, even here it is a folly to attempt to ascribe any grand narrative to the commercialization of science. It has been - and continues to be - a heterogeneous process; the interaction of long-term trends and political choices. 

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The academic career pipeline

Before proceeding any further, we must attempt to identify the concrete conditions of the scientific worker today. We can start with the actual career pipeline for the academic scientist. For those outside of the profession, much of this remains rather opaque. Scientists are rarely effective at communicating about either the content of their research or the nature of their industry (a 2024 Pew poll found that less than half of US adults think scientists are good communicators, and it's hard to argue with this). The expected career path in academia involves following up an undergraduate degree with a PhD. Together these are likely to take a decade to complete, though there is much variability. Following the PhD, the graduate will undertake contract work as postdoctoral researcher - a further training position. Having acquired further experience as a postdoc, the scientist will enter the highly competitive tenure track job market, where hundreds of scientists will be competing for a handful of jobs. Who gets the job will depend on a myriad of factors, including how good a fit the candidate is deemed to be, how appropriate their research programme is, their teaching experience and their publication record. The rewards are significant. Once one has acquired a tenure track job, they are on the road to a permanent position and significant job security. After several years as an assistant professor, a tenure review board assesses the scientist’s various achievements in terms of research, teaching and service. If deemed satisfactory, the scientist is tenured and now an associate professor. At this level, considerable job security has been achieved. The final step up is to full professor, based on significant research output and international reputation.

This professional academic pipeline is ever in flux, responding to a number of political economic factors. For instance, the postdoctoral position only came into existence in the United States after World War 1, and only really gained prominence when the state stepped in to fund postdoctoral positions via bodies such as the National Science Foundation (NSF) that was founded in 1950 (Kaiser 2005). The postdoc position has since become a necessary stepping stone in academia, spanning multiple years, and often multiple postdocs, before the scientist is deemed experienced and competitive enough to proceed onto the tenure track academic job market. A common justification for postdoctoral research is that it facilitates the sort of hands-on focussed training that the trainee scientist is somewhat sheltered from. Although it was not always so (prior to the hegemony of the postdoc pipeline, it was not uncommon to expect to attain an assistant professorship straight out of graduate school), the necessity of postdoctoral work is also a response to the increasing number of graduate students year on year, whilst the number of tenure track jobs has remained relatively flat (see Figure 2 below). Although postdoctoral research can be immensely stimulating (it is a unique phase in an academic career in which research is the primary focus, without heavy teaching loads or administrative burdens), they are also riddled with the same issues as any insecure contract work: an uncertain future, an inability to put down roots (postdoc contracts vary considerably in length), considerable dependence on ones relationship with their supervisor (who will be writing letters of recommendation when they hit the job market), and no guarantee of a stable secure job at the end of it all. 

Figure 2: from Schillebeeckx et al. 2013: Since 1982, almost 800,000 PhDs were awarded in science and engineering (S&E) fields, with only ~100,000 academic faculty positions created in those fields within the same time frame. The number of S&E PhDs awarded annually has also increased over this time frame, from ∼19,000 in 1982 to ∼36,000 in 2011 whilst the number of faculty positions created each year has not risen substantially.

What is evident from this brief sketch is that if one conceives of a pyramid with graduate students at the base, followed by postdocs, with the various tenured positions toward the top, recent decades have seen an ever-fattening base layer, whilst the hallowed upper echelons remain as narrow as ever. Combined with the increasing focus on funding applied science projects over basic research (see Figure 3 below), what we are seeing is an industry that is undergoing continual recomposition toward the ever increasing commercialization of science and - as we will see later in this essay - an ongoing process of the deskilling of the scientific worker. Crucially this is a process that has been ongoing well beyond the Trump administration’s recent attacks on the NIH. 

Figure 3: Inflation adjusted funding for applied and basic research, 2010 to 222. From  https://nexus.od.nih.gov/all/2023/10/31/trends-in-nih-supported-basic-translational-and-clinical-research-fys-2009-2022/

The science lab - structure and conditioning

The structure of the academic science lab is a reasonably straightforward hierarchy: at the top there is the principal investigator (PI), under whose supervision sits the whole lab. Next there are postdocs who are somewhat more independent than the next rung down, graduate students. Finally there are masters and undergraduate students, who may be joined to the lab for a brief project or for an entire honors thesis. There can be substantial variation within labs. Some will have staff scientists and lab technicians, both of which have considerable responsibility and authority. Indeed, because labs are largely shaped by the interests and motivations of the PI, the training experience will inevitably be different from lab to lab, with considerable variation in between. The academic science lab shares much in common with the petty bourgeois small business: there is a formal hierarchy that is often blurred through the sort of informal social interactions that are characteristic of small businesses in which bosses and workers interact on a daily basis (by contract most blue and white collar workers are unlikely to regularly - if ever - interact with the owners, board members, or CEOs of larger corporate institutions). Concurrently, there is a greater level of specificity within the academic science lab and small business, relative to the more uniform, juridically ironed out structures across large firms. In short, the tone and structure of the work environment is largely set out by the PI, with little oversight. Whether a lab member thrives, and the specific details of their workday, will predominantly depend on their relationship with - and the whims of - their PI. 

        In spite of these dynamics however, the academic lab is disciplined by external forces, just as the small business and the large corporation are. The currency of academic science always remains the same: publishing papers and being awarded grant funding. These are somewhat analogous mechanisms to the profit motive that disciplines businesses, in that if one wishes to remain operational in the academic research sphere, one must continually remain competitive by publishing papers and obtaining grant funding. Thus, the incentive structures are clear - utilize resources (both in terms of fixed and circulating capital, and - most pertinently - labor) as efficiently as possible to produce the maximum output. One might have a generous, considerate PI in the same way that a worker in an independently owned bookstore may have a generous and considerate owner. However the wider structural incentives will in both cases result in a tendency toward ever greater exploitation of the worker. Where the water gets muddied is that the temporal dimension is not always explicit, and indeed is getting more and more hazy. An 8 hour work day is quite concrete. Both inside and outside of academia however, more of the working day is happening off the clock. For academics this has always been a double edged sword, with the tilt of the blade largely dependent on the specific lab working conditions. Being able to manage one's own hours can be a great boon. On the flip side, it is a recipe for increased unremunerated labor, particularly when the worker is incentivised to churn out as many papers as possible to remain competitive on the academic job market. 

        To give one example of differing working conditions, a major categorical difference between science labs is that of wet and dry labs. Put simply, the wet lab is the lab we often see in popular culture - scientists working with specialised equipment, wearing white lab coats and safety glasses (those cultural signifiers). The dry lab is really just being sat at a computer, performing analysis on data that has already been generated. Where the wet lab is often (though not always) tasked with taking material input and converting it into data, dry labs often work downstream of wet labs, performing analysis on this data. Again, these are broad generalizations (much theoretical dry lab work is centred on producing analytical or simulated models for example), but they help us to begin to understand the differing nature of these two environments. The spatio-temporal elements are significant here. The wet lab is a physical space with highly specialised equipment, used for time sensitive work. The nature of the wet lab scientist's workday will to a large part be dictated by the materials they are working with. Thus, they are tethered to the lab space in a very concrete manner. By contrast, the dry lab is more concept than physical reality. Often the data has already been generated and thus dry lab members can work where and how they see fit, logging into VPNs to work from wherever is convenient. They are tethered to neither time nor physical space by the work itself. Instead, it will often be the expectations of the PI that dictate whether they must clock in at certain hours, and whether they must be physically present. Thus, both types of lab will be conditioned on the expectations of the PI, though also by the nature of the work itself. Indeed some PIs expect their lab members to clock in and clock out at the end of the day, whilst others are unconcerned with such spatio-temporal discipline, focussing instead on actual output.

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Revolutions in the content of work

Periodization is a fraught yet necessary endeavor. Fraught because history is too messy and heterogeneous to be confined to neat temporal categories; necessary to help us make sense of the world. We can periodize the changing labour process within a specific industry under capitalism as an initial period of the formal subsumption of labour, whereby increased profit is squeezed from the workforce via increasing the length of the workday. This of course has natural and social limits, in that a worker can only work for so long. As an industry begins to hit these limits, capitalists are incentivised to innovate to gain a competitive advantage over rivals. Thus, centralisation occurs as larger firms outcompete and absorb smaller firms via the productivity advantage given by newer technology, which in turn requires less labour to produce more commodities. This is the revolutionising of the labour process, known as real subsumption of labour. These processes were well described by Karl Marx (1867) over 150 years ago. However, we can also frame them in terms of the subsumption of science within the capitalist production process. Harry Braverman describes it thus:

“Science is the last - and after labor the most important - social property to be turned into an adjunct of capital. The story of its conversion from the province of amateurs, ‘philosophers’, tinkerers and seekers after knowledge to its present highly organized and lavishly financed state is largely the story of its incorporation into the capitalist firm and subsidiary organizations. At first science costs the capitalist nothing, since he merely exploits the accumulated knowledge of the physical sciences, but later the capitalist systematically organizes and harnesses science, paying for scientific education, research, laboratories, etc., out of the huge surplus social product which either belongs directly to hum or which the capitalist class as a whole controls in the form of tax revenues. A formerly relatively free-floating social endeavor is integrated into production and the market.” (Braverman 1974). 

Attentive readers will immediately note how the commercialization of science fits into this framework as the latest ongoing development in the reorganization of science. What we must ask ourselves is how these processes change the very nature of work, particularly in terms of the division of labor. Let us consolidate what we have so far:

1. The commercialization of science has resulted in a shift of emphasis and funding away from basic research and toward applied research, with a greater proportion of funding coming from private interests than has been the case previously.

2. The increasing number of graduate students and declining number of tenure track opportunities has resulted in an increased level of precarity at the earliest career stages within academia.

3. The currency of academia is publications and grant awards, creating incentive structures toward productivity above all else.

Taken together, we see a trend toward an assembly line model of the science lab, and a division of labor that encourages ever more niche specialization, to the detriment of a more totalizing view of any given research programme. More specifically, with a vast array of lab protocols and computational tools widely available, and access to an intensely competitive labor pool (due to the lack of secure job opportunities), a research pipeline in which specific lab members are focused on specific parts of the pipeline, with the PI masterminding the theoretical underpinnings of a given project will maximise productivity in terms of publications and grant funding. Thus, the worker is stripped of the subjective element of the labor process with this division of labor, a trend that is endemic to the capitalist production process:

“In its early stages, a new division of labor may specialize men in such a way as to increase their levels of skill; but later, especially when whole operations are split and mechanized, such division develops certain faculties at the expense of others and narrows all of them. And as it comes more fully under mechanization and centralized management, it levels men off again as automatons. Then there are a few specialists and a mass of automatons; both integrated by the authority which makes them interdependent and keeps each in his own routine. Thus, in the division of labor, the open development and free exercise of skills are managed and closed.” (Wright Mills 1951). 

It is worth emphasizing again that these are the incentive structures that exist, not a blanket condemnation of every scientific lab the world over. Indeed, tenure facilitates some level of “checking out” of the productivity fetish because a PI can maintain a stable career without having to worry about publications and grants. However, these are the measures of success, and thus ambitious PIs are incentivised to shift toward an assembly line model of science, and away from a space in which creativity and constructive failure are considered part of the scientific process. It is also important to emphasise that this isn’t just about PIs. In the absence of any countering narrative, this view of science has become dominant. As a trainee scientist in an intensely competitive industry, it is not surprising that you likely view your individual interests as tethered to a productive publication record. When the Russian biologist Leonid Margolis visited the US he observed that,

“Young scientists start to think that science consists of putting the results produced by one machine into another, and then into the next one, and of arranging thus obtained beautiful pictures and graphs into a publication.” (Margolis 1992)

What Margolis saw was a division of labour within the sciences that had stripped away the subjective element of the labor process and created the separation of specialists (PIs) and automatons (postdocs, graduate students etc). What we have described here is the process of the deskilling of the worker, a tendency that Karl Marx identified as inherent to the capitalist mode of production. Indeed, the trends we see in Figures 1-3 point to this very phenomenon. Our task now is to relate this all to the current moment.

Taken together, we see a trend toward an assembly line model of the science lab, and a division of labor that encourages ever more niche specialization, to the detriment of a more totalizing view of any given research programme.

The politics of science

The response from the scientific community to the Trump administration’s assault on scientific funding and structures has predictably centered on a moral and instrumentalist appeal to the value of scientific work. This is premised on a return to the linear model of science, in which there is a linear pipeline from federal funding for basic research, which informs applied research, which leads - via development of technology and scaling and marketisation - to tangible social benefits. The addendum to this model is one of science as a public good. Thus, not only can we attempt to quantify the value of scientific research, but we can also invoke its role in generating public knowledge that transcends quantification. Both these positions - science as quantifiably valuable, and science as a public good - fail to stand up to scrutiny. As Mirowski writes,

“The linear model held forth a promise that apparently nonmarket, nonaccountable (except possibly in some transcendental search for truth) activities could nonetheless cogently be tamed through a cost/benefit calculation, by “backward imputation” from the empirically observed value of the final goods to the virtual value attributed to noneconomic activities that had initially set them in motion.” (Mirowski 2011). 

In other words, the argument is that although we cannot a priori quantify the benefits of basic research, we can perform such quantification post hoc, and thus justify the public expenditure on basic research. Thus, it can be reasoned that science justifies investment in market terms. The linear model of science - whilst attempting to justify public funding - simply reinforces the commercialization of science by placing the market as the ultimate arbiter of merit. Whilst the linear model argues for an empirical-rational justification for the funding of science, the notion of science as a public good covers the moral argument, stating that science doesn’t need to justify itself as a profit making endeavor, for its contribution to the commons is as a qualitative good. An example of such argumentation is demonstrated by the tweet thread below. 

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I do not mean to target this individual specifically, but these tweets are a paradigmatic example of scientists failing to read the room. The entire argument hinges on a notion of science as some noble endeavor undertaken in purely altruistic fashion. And yet, what does it mean to better understand the universe when the fruits of such research are locked away behind paywalls in journals that profiteer at both ends, charging both to publish, and then to access those publications? The public are expected to accept this state of affairs under some abstract rubric of progress. It is important to note that few lament the state of academic publishing more than academics themselves, and I will return to the question of publishing later. For now however, the point worth emphasizing is that asking the public to have faith in the idea of science as a public good without that same public having the capacity to directly scrutinize scientific research is the sort of hubris that is grist to the mill for anyone looking for easy anti-establishment narratives; narratives that Trump has proven remarkably adept at weaponising. 

        This begs the question that really sits at the heart of this essay: Why are scientists so ill-equipped to deal with Trump’s assault on science? This is ofcourse a political question, and scientists have consistently vacated the political space when it comes to their own field, in no small part due to the hegemonic notion that science is an objective pursuit, shorn of ideological interests and driven by the search for truth. This runs counter to every aspect of science, from the day to day activity of the scientific process, to the high level decisions on how research is funded. This notion of an apolitical science is not just limited to scientists themselves, but across the scientific terrain:

“The making of evaluative decisions and the exercise of authority or advisory authority is a pervasive fact of scientific life: in directing the work of subordinates, in asking funding bodies for resources, and the like. But this political ‘decision making’ character of science is also a largely underdiscussed fact - whether by commentators on science, philosophers of science, or sociologists of science. The reason for this neglect, in part, is that these decisions occur under a particular theory or ideology: the idea that the scientists making the decisions are operating neutrally or meritocratically, and that the public role of science itself is neutral. Science is thus mundanely political, but its overtly political features are conceived to be unpolitical.” (Turner 2002).

Many scientists will have experienced the sinking feeling of receiving a scathing peer review in which a paper is outright rejected, before submitting to a different journal and having that very same paper sail through peer review in glowing terms. Scientists are partisan, ideologically biased, and politically motivated, and science is a social process. By conceiving of science as apolitical however, the response to obstacles is always a technocratic fix. Birukou et al.’s (2011) review of alternatives to peer review is a good example of this. Discussed are a range of tweaks and workarounds to the peer review process, without ever touching on the specific relations between journals and the labor that they exploit and profit from. The same might be said for endeavours such as the preprint server arXiv - useful in its own right, but merely an appendage to the peer review publication machine that is utterly hegemonic (as usual, Mirowski (2018) has a comprehensive commentary on how Open Science has largely been a vehicle for the corporate tech sector to impose social-media style platformism on the sciences). Because the currency of science is publications, a truly radical alternative cannot simply coexist in the same ecosystem. Instead, this is a political question, rooted in power dynamics within the corporatised sciences.

The Trump administration's current assault on academia has brought some of these latent questions out into the open. Nowhere has this been more acutely felt than at Columbia University. On March 10th, the NIH announced that $250 million in funding across over 400 individual grants was to be pulled from Columbia, at the behest of the Joint Task Force to Combat Anti-Semitism. This was a direct reaction to the pro-Palestine encampments which were first set up at Columbia in April 2024, catalysing a raft of further encampments at university campuses across the US. A day after this announcement, Mahmoud Khalil - student activist and negotiator on behalf of the Columbia encampment - was abducted by ICE agents from his apartment. The agents produced no warrant, removing Khalil - a green card holder - from his apartment and transporting him to LaSalle Detention Center in Louisiana. Khalil has thus far not been charged with any crime or been deemed to have engaged in any illegal activity.

The thread connecting the encampments and the abduction of Khalil to the stripping of NIH grants from Columbia scientists requires little scrutiny to identify, and yet the response of the science community has not been one of political engagement, but one of distancing, often in a very literal spatial sense, on the grounds that biomedical research is conducted on a different campus than that on which the encampments were set up! Instead the focus has been a moral appeal to the importance of the work being conducted at Columbia, and the altruistic nature of scientists at all levels. One is tempted to ask who this appeal is to. The Trump administration and the wider MAGA movement have clearly identified academia as an ivory tower of elitism, and have no interest in moral appeals. Besides which, why should the wider academic community - nevermind anyone else - stand with scientists against this assault when there is a collective silence around the abduction of a member of that community? There is a very obvious line running through the genocide in Gaza to the stripping of science funding in the US, and an abject failure on the part of scientists to draw attention to this line, for fear of suffering from the backlash both from the state and from their peers. This fear is somewhat understandable given livelihoods are at stake, and a single voice speaking out is unlikely to make a material impact. What this highlights however is the complete absence of collective power that would allow these threads to be drawn together into a coherent narrative critique, as opposed to the fear any individual has of exposing themselves to opprobrium. This is the focus of the next section.

Academia, class and solidarity

It is not unreasonable to ask why any academic should stick their head above the parapet and link these issues, given the Trump administration’s swift retaliation to any dissent (student visas are being revoked daily in reaction to any dissenting views on social media). We might rephrase this question: why is academia so weak as to be unable to produce material solidarity with those within its ranks that are having their lives overturned, be it via loss of funding or - far more consequentially - deportation? Again, we are faced with a question of power. Throughout the history of capitalism, workers have banded together in collectives (formally, though no exclusively, via unions) to struggle for better working conditions. A single worker that demands a better wage or a safer working environment is easily replaced. A whole workforce is much harder to dislodge, particularly when they exercise power by withholding their labor during a strike. Ofcourse, a successful labor action requires opportune conditions, including but not limited to a level of workplace solidarity that ensures strikes will not be undercut by scabbing (workers refusing to join the strike and continuing to work) and a set of concrete demands. Universities are no strangers to both radicalism and strike action. There are spatio-temporal aspects of university life that facilitate collective action. These are uniquely densely populated spaces, with more flexible demands on time than in other workplaces. However there are important differences between student radicalism and the sort of ongoing building of solidarity that cuts both beyond the physical university space, and the academic hierarchy. Student radicalism is ephemeral by its very nature, specifically because students are passing through the university space over a short number of years. Thus, such radicalism is rarely able to reproduce itself. Within an even shorter timescale, each academic year is broken up by a long summer holiday during which students will disperse from campuses, diffusing any discontent that may have been percolating over the preceding months (this point makes the heavy handed response to the pro-Palestine encampments by university administrators and the police particularly puzzling, given that they could have waited activists out until the nearby summer break, whereupon the size of the encampments would inevitably collapse). 

The question of longer term organizing and reproduction of collective power must look beyond just students, and to academics themselves. The inability of academics, and indeed white collar professionals to organize and show material solidarity with their fellow workers is often explained by their being part of what Barbara and John Ehrenreich called the Professional-Managerial Class, or PMC. In their 1977 essay, they defined the Professional-Managerial Class as,

“Consisting of salaried mental workers who do not own the means of production and whose major function in the social division of labor may be described broadly as the reproduction of capitalist culture and capitalist class relations….Thus we assert that these occupational groups - cultural workers, managers, engineers and scientists, etc. - share a common function in the broad social division of labor and a common relation to the economic foundation of society.” (Ehrenreich and Ehrenreich 1977).

A great deal of subsequent work has added to this initial theorization of the PMC, mostly focusing on the habitas of professionals. To take the example of academia, those in the field have to be geographically mobile to have any chance of obtaining a secure career within their field of interest. The number of positions are small, temporary, and - as already discussed - with little job security. A graduate student might find themselves undertaking their PhD on the other side of the country, then leaving the continent to undertake postdoctoral research, and finding further geographical instability when searching for a tenure track job. This spatio-temporal dislocation is anathema to putting down deep roots and building community. The competitive nature of academia also encourages the centering of the individual over the collective. One's colleagues are also one's competition for an ever more scarce number of jobs. The question of building the bonds of solidarity that translate into collective power then cannot be answered without accounting for these habitual constraints.

        One might conclude from such an analysis that attempting to overcome such habitual constraints is futile. However, it is worth emphasizing that these constraints are questions of habitas, more than a rigorous class analysis. This is not the place to relitigitate the patchwork landscape of Marxist and - patchier still - non-Marxist theories of class. It is important to note however that the majority of academic workers are proletarianized. That is, they must sell their labor power (i.e. their capacity to work) in order to reproduce themselves (i.e. purchase the goods needed to survive). The PMC is not a distinct class category. However, it does provide us with an analytical framework for understanding why this cross-class stratum of salaried mental workers acts within differing incentive structures than what might be called - for want of a better term - the traditional working class. The main take away here is that these incentive structures help us explain the lack of solidarity and collectivity across mental workers, and - pertinently - that these barriers are not hard structural barriers that cannot be overcome. They help explain our present. They need not explain our future. To paraphrase John Holloway, if the professions were characterised by the total objectification of the subject, then there is no way that we, as ordinary people, could criticise our alienation. What then might a path forward look like?

The competitive nature of academia encourages the centering of the individual over the collective. One's colleagues are also one's competition for an ever more scarce number of jobs. The question of building the bonds of solidarity that translate into collective power then cannot be answered without accounting for these habitual constraints.

The utopian and the concrete

How are bonds of solidarity formed vertically across the academic hierarchy, and horizontally across industries? As always, there is a conjuncture-agency dialectic that must be navigated. How much individual agency we have is both shaped by, and is constantly shaping the material conditions in which we operate. The conditions seem bleak. Looking beyond the habitual obstacles to solidarity within the academy that I have identified in the preceding section, workers have been on the backfoot for several decades, often engaged in defensive struggles to maintain some semblance of reasonable working conditions against the neoliberal agenda of deregulation and erosion of job security. Against this backdrop there is no out-of-the-box viable strategy for workers inside or outside academia, that if applied properly would guarantee the improvement of the lot of workers. In a nutshell, emancipatory agency is limited in the current moment. 

The first step then is to identify what our goals are, both short-term in the face of attacks on science from the state, and long-term with regards to a truly emancipatory and radical reshaping of science. This must of course be a collective dialogue. It is all well and good lamenting the state of peer review, and the unsustainable decrease in secure jobs in science relative to cheap graduate student labor. It is quite another to start thinking about what science could actually look like, shorn of the profit motive. Thus, some level of utopian theorizing is necessary. For example, in The German Ideology, Marx envisioned a society in which,

“nobody has one exclusive sphere of activity but each can become accomplished in any branch he wishes, society regulates the general production and thus makes it possible for me to do one thing today and another tomorrow, to hunt in the morning, fish in the afternoon, rear cattle in the evening, criticise after dinner, just as I have a mind, without ever becoming hunter, fisherman, herdsman or critic.” (Marx 1845).

It is easy to dismiss such a statement for its irrelevance to such specialized forms of labor such as science and engineering. One cannot simply pick these up and put them down at a whim. However, it is far more generative to take up Marx’s vision and seriously consider how it might be applied to the sciences. What does a truly democratic version of the scientific process look like? It must surely be a far cry from the current structure in which the majority of the public are locked out of science both by paywalled journals and by the fact that scientists are incentivised to be effective at communicating their work to their peers (via peer review and grant panels), rather than to the public. 

        Notice how even this preliminary consideration of Marx’s utopian vision has opened up space for critique of the concrete structures in which science is conducted today. In some ways the negative critique is straightforward enough - much of this essay is concerned with negative critique of the practice of science as it currently exists. Much more difficult is the positive vision. I do not intend to paint a comprehensive forward vision here. Instead a few broad brush strokes shall suffice. The fundamental point about notions such as “every cook can govern” is not that everyone has the aptitude or interest to pursue scientific research. This is a rather uncontroversial point, and so is the notion that anyone who does have an interest should be able to engage with scientific practice. I am not exclusively concerned with equality of opportunity here, but with how science is a social endeavor that must be an ongoing dialogue between social actors. Just because someone doesn’t wish to commit years of study to perform scientific research, does not mean they should be shut out of science entirely. To democratise a system means to nurture engagement within that system. It is worth briefly noting that most scientists speak in glowing terms about SciHub, which has democratised access to science papers. This is no mere technocratic fix. This has materially changed how scientific knowledge is distributed, and its importance cannot be understated. However SciHub alone is ofcourse not enough. Democratisation of access is necessary but not sufficient. We must change how individuals relate to science too. Everyone with the inclination and time is capable of grasping the key debates in any specialised field of study, and this is the crux of the matter. Inclination and time. We have rather innocuously found our way into one of the simplest and most radical of demands: that of the increase and control of our own free time. Again from Marx:

“Free time – which is both idle time and time for higher activity – has naturally transformed its possessor into a different subject, and he then enters into the direct production process as this different subject.” (Marx 1857).

This is the overcoming of alienation and the unleashing of the creative potential of the individual: scientists no longer just brainless cogs in an assembly line, and science as a property of the commons, not just of profit-seeking entities and tenured professors. It takes no great leap to understand how such an outlook facilitates engagement with basic science as an important end in itself, and not just as a necessary cost to facilitate downstream profit-generating projects.

A key task then is to flesh out these ideas of what a democratized radical science might look like. We must be utopian in the sense of envisioning another world, yet concrete enough to understand the processes, logistics, supply chains, and funding sources that currently make up science and would have to be accounted for.

Democratisation of access is necessary but not sufficient. We must change how individuals relate to science too.

The current moment

I suspect that some readers may at this point be thinking that its all well and good to theorize utopian futures, but that does little to account for the here and now, and the Trump administration's slash and burn approach to state based scientific agencies. The problem is that no institutional infrastructure has been built to withstand these attacks. Thus, there is no collective power. Exploring the reasons for this lack of collective power has been a principal concern of this essay. When a PI hears of grants being cancelled at a university on the other side of the country (or indeed even in the same institution), the forums to express their concern are social media, or through university mediators. There is no independent forum where scientists can collectively think through the current moment, and collectively respond. And it must be emphasized that collective responses must go beyond public letters, which may highlight moral objections but also highlight a lack of power to do anything about anything. Collective power is the collective ability to down tools; the collective ability to show what happens when scientists en masse reject funding cuts or exploitative peer review or early career precarity. What’s more, it is political power. Whether scientists wish to acknowledge it or not, they are political actors by definition, and it is within the political sphere that the assault on science is occurring. 

This collective power is not easily built. We have seen it eroded across much of the globe as geographical, financial and job stability have been slashed. As I have shown, it is perhaps even harder to build in professional vocations such as science, where mobility and competition incentivise individualism to a qualitatively greater extent than in many other industries. However that does not mean it is impossible. There is a direct link between building intra and inter-industry bonds of solidarity, and the thinking through of the utopian visions discussed in the prior section. Technocratic tweaks that sidestep politics will not save us. Only collective action stands any chance.

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