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Conclusions

Part 3 of ‘The Foundations of Academic Organization’ blog series

There is nothing new in intellectuals focusing on narrow fields, as Johnson’s comment about eighteenth century specialisms reminds us. But now the number of fields has never been greater — 170 sub-fields across 19 main subject groupings, according to the UK’s government audit of research, the Research Excellence Framework. And academic subjects have never been more entrenched in their own separation. ‘The first point of identity for most academics is commonly acknowledged to be their disciplinary community or “tribe”’ (McFarlane (2005, p.307; and see Becher and Trower, 2001). Modern disciplines reflect

Disciplines create the predominant focii for research funding, exchange of ideas and publishing. And they are avenues of career progression and mutual evaluation.

In modern academia, disciplines typically have three main ‘zones’ marked off from each other by different kinds and pace of changes in their intellectual development (Figure 2.2). The first is a ‘dead heart’ of already developed, thoroughly explored and perhaps ‘worked-out’ subjects. Crucial for teaching and graduate education, this disciplinary core is relatively unchanging. Research in this area may effect incremental alterations, but will typically depend on mobilizing large-scale grants or resources, and in some STEMM subjects undertaking extensive collaborative projects.

One common way in which disciplinary ‘tribalism’ can be fueled is that (left to themselves) academic departments or labs controlled by dominant coalitions (with long-lived staff on tenure track) tend to over-focus on the core ‘dead heart’ area, with most new research clustering on the internal boundary with the zone of moderate advances. Once established this pattern is easily replicated as PhD students and early career researchers are pressed to ‘fit the mould’, while the faster-moving boundary and cross-over areas get neglected. Faculty may tend to ‘man the barricades’, repudiating insurgent ideas from other disciplines and offering dismissive estimates of the prospects for gains from co-operation with colleagues in other subject areas.

Figure 1: Three main zones inside academic disciplines

Yet very rarely is the ‘dead heart’ all there is to the discipline, still less the most intellectually dynamic part of it. Instead, around the heartland area there first lies a zone of moderate advance (shown pink in the Figure). Here ideas are still evolving at a smarter pace. Smaller scale research projects and PhD work are typically concentrated here.

Finally, it is normally at the disciplinary fringes adjoining and overlapping with other academic subjects (shown as purple, green or orange shaded in the Figure) that the pace of advance and innovation is typically fastest, amidst a good deal of intellectual ferment. Younger and more daring researchers will often concentrate in these newly evolving specialisms, constantly having to combat the ‘old guard’ critique that they are diluting the discipline’s pure central stream.

Whatever the discipline in which it is undertaken, all academic work can be thought of as contributing strongly to a ‘dynamic knowledge inventory’ — a huge and central pool of knowledge, information, expertise, and technologies (Bastow et al, 2014). It contains multiple possible solutions for myriads of actual or possible civilizational problems, and its care and management are hallmarks of a developed society. The inventory is dynamic because it is constantly changing, with new expertise being added and older or less used bodies of knowledge falling out of recognition or recall.

Only a small proportion of the potentially available knowledge is normally being drawn down and used at any given time. But advanced industrial societies in particular have a capacity to rapidly access (or develop) many appropriate solutions, even for radically new or highly intermittent problems. From this reservoir, skilled and motivated businesses, governments, civil society organizations and citizens alike can all draw on information to help them solve problems, plan ahead and conduct or improve their everyday operations. And as Daniel Dennett noted: ‘Scientists have power by virtue of the respect commanded by the discipline’. So this draw-down process has especially favoured well-organized science and technology disciplines.

Universities are not the only, or even the largest, contributors in building the knowledge inventory. Businesses, government agencies, professions, civil society organizations and knowledgeable individuals in many walks of life all contribute. But academics play key roles in testing, conserving and organizing established knowledge, and also in constantly enlarging the inventory by adding new ideas, techniques, and capabilities.

A knowledge inventory is also almost weightless and multi-dimensional. Unlike a physical inventory, the DKI is not costly to maintain for individual contributors and users. And its components have value even when the knowledge involved is not being actively used. The huge stock of latent knowledge included provides additional options and capabilities when needed, the ‘strength in depth’ that especially distinguishes advanced industrial societies from others.

As Figure 2 below shows, the university contributions to the knowledge inventory come in two broad groups, the first and largest taking place inside single disciplines, shown on the left of the Figure. I leave the consideration of the joined-up processes to the next post, and so just focus here on the dominant effects from individual disciplines in each area of work.

Figure 2: How both single-discipline and more joined-up scholarship build up the dynamic knowledge inventory and generate impacts from academic research

Even as we focus down on any particular discipline or subject field, however, it is important to recognize that these fields of endeavour each encompass a number of different types of scholarship. An influential fourfold typology by Ernest Boyer (1990) is shown in the single-discipline box on the left of Figure 1, and in the rest of this post I shall re-package and somewhat renovate Boyer’s categories .

Popular misconceptions imagine that academics are pre-occupied by only one of these types, ‘discovery’ research. So they often give a naïve view of what it takes to make a discipline work. Against this misconception Boyer stressed three other scholarship processes included in the leftmost box — focusing on achieving intellectual integration into coherent wholes, applying academic knowledge to practical issues and problems, and renewal of a given academic profession itself.

Boyer focused on the deeper-lying purposes or rationales organizing academic work within a highly differentiated grid of knowledge specialisms were well explored by Boyer (1990). He stressed that knowledge advances are not a simple matter of making isolated new discoveries — important though these may be. Rather, the development of disciplines is inherently bound up with other key functions — including a scholarship of integrating knowledge across disciplines, a scholarship of applying knowledge in service to wider societal goals, and a scholarship of what Boyer termed ‘teaching’, but which we see more broadly below as ‘renewal’ of the profession. These key insights have resonated widely in the field, and we follow them closely (albeit in a somewhat adapted form) in what follows.

Discovery research seeks to produce specific ‘new facts’ or original insights — although appreciating what is new or significant is itself complex. As Abraham Pais noted: ‘To make a discovery is not necessarily the same as to understand a discovery’. Discovery is by far the most mythologized of scholarly activities, not just in layperson views imagining ‘Eureka’ moments, but even within academic professions themselves. This kind of scholarship is conventionally closely associated with innovation in findings or understanding relationships. Core activities here are shown in Figure 3.

Figure 3: Main components of the scholarship of discovery

Key discovery research methods include:

- Experimental science, uncovering or replicating previously unknown effects under tightly controlled experimental conditions in laboratories. Most physical sciences follow ‘reductionist’ strategies, trying to understand components at the smallest feasible scale, and with knowable laws allowing (relatively simple) aggregation of components.

- Randomized control trials (RCTs) extend an experimental approach to natural, computer, internet, human or societal environments where lab conditions cannot be replicated, because multiple causal relationships are in play simultaneously. Holistic (often chaotic) phenomena here are rarely simply predictable sums of component influences, due to interactions, emergent effects and multiple-level influences.

- Field trips to uncover ‘new facts’, such as expeditions seeking to identify new species or expand knowledge about rare ones, archaeological digs, archival research in history or literary/ cultural studies, and many related forms of investigative effort.

- Database analysis, where already-collected information is interrogated using new mathematical algorithms, models or methods, often drawing on new theories, hypotheses or chains of deductive reasoning. Across all subjects (including the humanities) the scale of modern databases has mushroomed, and internet and web access make information far more globally available.

- New theory development, ranging from focused cogitation in producing maths formulae and theories, through progressively ‘softer’ or more intuitive/interpretive forms of theoretical abstraction and innovation.

Most academics’ and almost all outsiders’ concepts of ‘real’ research focus either on the first item in the first box of Figure 2.3 (core experimentation) or on authoring/writing time — both seen as critical for creativity, ‘genius’ and ‘break-through’ innovations, especially in the physical sciences and technology. Even our listing of ‘core’ activities occupies only the top box in Figure 3, although that shows that the wider landscape of ‘discovery’ activities also includes many elements of academic citizenship, academic management, teaching and achieving impacts. An inter-generational division of labour is often observed in academia, with new discoveries often made by experienced or talented younger researchers, while older academics focus more on creating the multiple conditions for discovery to happen and be understood, and on integration scholarship (considered next below).

Despite its salience and prestige, discovery scholarship, in all its forms above, is not the ‘be all and end all’ of academia. It is not the only ‘core’ research process, set against which all other activities are secondary or dispensable. To do effective discovery research, or even just to secure a few hours of ‘core’ research time to themselves, academics need to create and run well-organised labs and departments; acquire and set-up equipment or access to data; perfect methods (often through laborious trial and error); establish research protocols and ethical permissions; obtain access to relevant survey respondents or sets of personal data; organize field trips or archive visits; create research traditions and detailed institutional expertise and memory; immerse themselves in other people’s forefront research; transfer knowledge; work on publications; organise and attend conferences; develop research grants to escape from teaching for a while; and supervise doctorates. These activities are in no way separate from discovery. They form integral parts of the process of uncovering new knowledge.

In How Institutions Think (1986), the anthropologist Mary Douglas also stresses that it is the professions, research laboratories and academic departments, journals, conferences, funding bodies and other related organizations that jointly govern the internal recognition of ideas in any discipline as novel and worthwhile. Many other, ‘boundary spanning’ organizations (and even individuals) (some involved in the impacts interface discussed in Parts III and IV) control the rate at which innovations and ‘worthwhile’ discoveries are picked up outside academia. In the digital era the scale of such organizational filtering (which Douglas determinedly insists on calling ‘organizational thinking’) is often international, and sometimes global. Yet all too often academics themselves naively dismiss these organizations’ filtering key roles as so much dispensable ‘bureaucracy’.

In the past discovery processes in the physical sciences were far more closely linked to specific technological changes and industrial application imperatives than they perhaps are today. In the era of the most rapid scientific advances (from the 1600s onwards), there were close and integral linkages between pure science, applied science, and technology, especially in wartime — with developments in practitioner fields strongly influencing new scientific advances. Since World War II in many contemporary ‘big science’ fields (especially those covering natural systems, such as particle physics, astronomy or astrophysics), this linkage has been decisively severed. Instead the only conceivable ‘paying customer’ for much forefront research has become national governments, although commercialization of fields like space technology or the internet can happen once serial government funding injections break down long-standing tech barriers (Mazzucato, 2018; National Research Council, 2012). However, in ‘human-dominated systems’ (such as the medical sciences, engineering and computer sciences, design disciplines and the social sciences) a far closer binding of discovery to application persists. For instance, a ‘big pharma’ nexus links multi-national drug companies with medical academics and university hospitals across many countries — providing perhaps the best example of the inter-penetration of industry and academia in knowledge discovery. This reflects the high capital and implementation costs of drugs research, and of meeting regulatory approvals via animal testing, randomized control trials, and other approaches. But important and quite similar clusters occur in knowledge sectors close to defense industries (such as aerospace and materials science), nuclear energy, bio-sciences, agribusiness and cutting-edge information technology.

Corporately organized and team-driven discovery scholarship will have most impact on business where it creates a competitive advantage yielding legally protectable intellectual property assets (like patents), and more immediate or potentially ‘cashable’ gains (Gertner, 2012). Manufacturing and large services firms (as in IT, internet companies and telecoms) expend significant resources in monitoring disciplines where the predominant patterns of knowledge advance mean that such discoveries most often occur, especially in STEMM disciplines. Governments generally follow suit less intensively in pre-commercial areas, but are most active in the defence and medical areas.

Conventional and layperson views often seem to suppose that academic research primarily changes the dynamic knowledge inventory directly via individual or team-based single discoveries. Most media stories (including media releases originated by universities themselves) focus on discrete new findings or inventions allegedly making possible new or changed products or practices in firms, medical services, or even government. A more realistic picture recognizes that even in the physical sciences unmediated (or even clearly traceable) effects from individual research outcomes are very rare. Typically, dozens of cumulative contributions from many different kinds of research have to be brought together by firms or agencies with working knowledge of complex issues to effect any practically useful changes.

We expect discovery research to have much more slender and only episodic influences outside the university sector itself than the conventional wisdom envisages. A great many academic discoveries are inwards-facing to one discipline. They concern the ‘swarms’ of methods, techniques, equipment, routines, and standard operating procedures of academic research itself. Hence, in any discipline, relatively few ‘discoveries’ can be successfully explained or ‘sold’ to an elite audience outside. Even fewer can reach general media or achieve any widespread dissemination (such as the results of new medical or drugs trials) — despite the often misleading efforts of university media offices (see Improving the Impacts of Academic Research, section 8.3; ). Conflicting results and scientific controversies often take the edge off initially promising findings, almost invariably showing that problems, exceptions, by-product effects and possible solutions are more complex than they may appear at first sight.

In human-dominated disciplines also, the rapid evolution of new behaviours by societies and social groups (for instance, the spread of new social practices, use of new internet tools or new environmental threats) often outpaces academic knowledge (Dunleavy et al, 2014). Researchers here can be scrabbling to catch-up with unforeseen changes in social practices and even natural environments, perhaps with few special claims to expertise about them. Across most of the social sciences (and some parts of all human-dominated systems) even the possibility of specific discoveries (especially ‘breakthroughs’) can also be questioned. Developments here rarely follow social ‘laws’ that can be authoritatively validated by ‘professional social enquiry’ or PSI, as Lindblom and Cohen (1979) term it. Instead change is incremental and PSI-validated evidence commonly forms only scattered pinpricks of high quality knowledge across a wide research or policy canvass. Joining-up the dots is essential to find a pattern of any use, but here the only frame for this is ‘ordinary knowledge’. This knowledge consists of specific and valuable expertise of many kinds. It may include some forms of ‘common-sense’, but also many forms of knowing and much accumulated wisdom that contradicts ‘common sense’. Ordinary knowledge may often be well-developed (even esoteric), making sense intuitively or being demonstrably helpful. But it has not itself been scientifically validated. In social systems it is only by combining scientific and PSI results with developed forms of linking ordinary knowledge that we can achieve ‘useable knowledge’.

How academics and intellectuals absorb, understand, synthesize, and connect new knowledge garnered via discovery research into coherent theoretical and interpretive knowledge frameworks is critical for many reasons, as the management theorist Henry Mintzberg commented: ‘No generalizing beyond the data, no theory. No theory, no insight. And if no insight, why do research?’ Figure 4 shows a summary of the many activities that go towards this process within a single discipline. Core activities span across the five conventional academic roles, since the disciplinary integration of ideas operates at many levels.

Figure 4: Main components of the scholarship of integration

Although discovery findings are vital, taken on their own they are often not easy to make sense of or act on. At many levels (and not just the conventionally understood macro level), the modern philosophy of science stemming from Thomas Kuhn (1996) argues that all the STEMM sciences and social sciences are shaped by ‘paradigms’, integrating conceptions that help to explain the body of scientific knowledge in the relevant area as a whole. Scientists (and academics in any discipline) often tolerate an extensive accumulation of ‘anomalous’ results inconsistent with the prevailing paradigm. When they cannot be fitted into the accepted, dominant synthesizing paradigm of theoretical explanation, unexpected empirical results are often not taken to directly falsify the current paradigm, but instead often side-lined as ‘puzzles’ needing future solution. This situation may persist so long as there is no competing alternative paradigm that can make sense of both ‘mainstream’ observations and theory, and these known but marginalized ‘puzzles’. In the sciences especially, only a new paradigm can (sometimes) shift established ways of thinking within a discipline.

Integrative work is key in academic disciplines because most intellectual controversy focuses on what is not yet known or agreed. In almost any subject a currently hegemonic paradigm provides a form of professional ‘conventional wisdom’ or the mainstream view. It is often still being critiqued by at least one older ‘legacy’ view, a perspective that was previously hegemonic but has now faded in appeal. Such historic challenges weaken very rapidly in STEMM sciences, but not in the social sciences. The mainstream position is also normally under attack by one or more newly ‘insurgent’ approaches.

Modern STEMM disciplines often have the fewest distinct theory positions (or ‘schools of thought’). They show the least visible professional dissensus internally, following a ‘high consensus, rapid discovery’ pattern where the discipline focuses closely on the research frontier, with prestige attaching chiefly to pushing that forward (Collins, 1994). STEMM disciplines move very swiftly to a strong consensus view of phenomena lying within the frontier itself, and assign lesser status to any remaining controversies there. ‘Softer’ (less mathematical) disciplines, like the humanities and some social sciences, have more enduring debates between long-enduring ‘schools of thought’. In pure humanities disciplines (like philosophy or literature), inter-theoretical struggles still define the ‘commanding heights’ of the discipline. Randall Collins (1998) suggests that an ‘intellectual law of small numbers’ applies in all disciplines, limiting top-level positions to between two and six — two because one cannot disagree with oneself; and six because the ‘Law of Small Numbers’ suggests that any greater differentiation erodes the ability of all the positions in conflict to attract support and to survive inter-generationally.

The importance assigned here to integration scholarship within each discipline also recognizes that most key advances come out of supportive academic environments in which a wide mix of activities, people, skills and favourable organizational structures encourages radical innovations in knowledge structures and ideas and connections. Perhaps the most productive integrator in modern science history was the Nobel-prize winning physicist, Lord Rutherford, whose skillful direction of laboratories at Manchester and Cambridge helped eleven of his close colleagues to win the same prize, across three ‘miracle’ decades for the expansion of physics as a discipline, from 1898 to 1932 (Reeves, 2008). The collective character of integration advances also explains why integrative (and not discovery) social science scholarship has by far the most influence on cultural systems, and knowledge processes in civil society, business and media.

Basic theory and empirical knowledge garnered in lab conditions may be well-established, but applying them in concrete and markedly different physical or social situations is far from straightforward. As the history of bridge catastrophes demonstrates, even the best understood knowledge must take full account of the uniqueness of a specific environment, where dozens, hundreds or thousands of system interconnections, constraints and causal processes operate jointly and interact with each other.

The scholarship of application tackles this key set of tasks, but was often looked down on in the past. For example, Henry Rand Hatfield commented in 1924:

Figure 5 shows that successful application work also involves many strands of academic activity. All these elements help the specification and differentiation of basic knowledge and research so that it can be meaningfully used to reach an acceptable solution to practical problems and social demands. The originality of such efforts should never be under-estimated. As the mathematician Georges Pólya argued: ‘A great discovery solves a great problem, but there is a grain of discovery in the solution to any problem’ (1998, p. v).

Figure 5: Main components of the scholarship of application

In concrete terms, application scholarship is a substantial share of new grant-funded research in every discipline. It also connects closely to a swathe of university work on externally-defined problems, including basic research and consultancy in government and corporations, design work and prototyping. The complex development of modern civilizations entails that an ever-increasing proportion of academic work is now concerned with ‘human-dominated systems’, spanning beyond what Herbert Simon (1996) called the ‘sciences of the artificial’ and including all the social sciences (Bastow et al, 2014). In such fields the vast bulk of work may fall in or close to the application category, since the development of new knowledge may not change the ‘first principles’ science base much. Instead it primarily extends the remit of basic knowledge to cope with constantly developing forms of human-generated artefacts and social situations

The scholarship of application is particularly important in the modern world — where medicines ‘wear out’ as the organisms targeted develop resistance; where businesses need innovative product features to create competitive advantages; and where public policies must constantly develop in an ‘agile’ fashion to counteract civil society’s capacity to find countervailing responses to previous state interventions. We expect applications scholarship to have far broader and more immediate influences on both business and public policy than the two forms reviewed so far — principally because it tackles more proximate or immediate problems for these sectors. Inherently it must often do so in more ‘joined-up’ ways than highly siloed ‘pure’ research (see Improving the Impacts of Academic Research, Chs 6 and 10).

Finding and developing new people with the talent to become senior or experienced scholars is a constant scholarly task in each academic discipline, and in outside professional groups linked to it. The scholarship of renewal inevitably absorbs a large part of academics’ time. And because young researchers are often the most inventive, it connects in integral ways to the vitality of discovery, integration and application.

Figure 6 shows that key tasks here include developing research-led teaching, and helping to supervise and socialise PhD students. Bringing on young researchers in the field is closely bound up with the management of research laboratories and academic departments, together with the creation of inter-university institutions and linkages that can sustain a decentralized process of talent management. The close involvement of senior academics is essential to how all these processes work out.

Figure 6: Main components of the scholarship of renewal

The varying success of different universities and different countries across disciplines closely reflects their levels of investment in renewal processes, and their ability to master the sophisticated knowledge transfer and knowledge management approaches needed for disciplines to grow and flourish. The scholarship of renewal also has a strong and slow-to-change influence on overall academic ‘culture’. In many science disciplines it also has close links with the culture of government and corporate research labs, through them exerting a key influence on overall national R & D achievements.

More broadly though, renewal activities are constantly shaped by the demands of the wider economy and society, since in every discipline academic departments necessarily provide education for undergraduate and even Masters students, most of whom do not go into universities or even other research occupations. Especially for disciplines dealing with human dominated systems, which are in practice almost wholly applied in orientation, there is no hard and fast line between what is needed for outside employment or vocation and what is needed for academic study. Currently in the US more than half of undergraduates complete vocationally orientated degrees, rather than traditional, academically-defined qualifications. The necessary interaction of academic departments with employers in such subjects entails an extended liaison between university teachers and businesses or government.

The scholarship of renewal also carries within it a stream of impacts that are not easily recordable or traceable in the electronic footprints of the digital era, but are none the less real — namely the carrying over of education and socialization from university courses to other sectors, by students moving out of universities and into different occupations. These effects and consequences do not feature further in this analysis, but they are none the less large-scale and important ones, and they operate over considerable periods of time.

Looking at the four forms of scholarship together, in each discipline they cross-influence each other, as shown by the flows inside the fawn box in Figure 2 above. The closest and most frequent feedback is likely to be from discovery scholarship to integration, as new results and relationships expand and morph into accepted understanding in the discipline. In turn, integration activities mostly select (or discard) avenues in discovery, while new theories, ideas, memes and juxtapositions of knowledge suggest a flow of new experiments, field investigations or data analyses that can be attempted. In STEMM subjects especially, many key discovery developments take the form of searches for theoretically predicted effects.

Second, we expect to see a constant and relatively direct feedback loop operating between discovery and application activities. In many STEMM disciplines there are possibilities for patenting processes and applications, extending also to spin-out companies from universities, and increasingly facilitated by expert sections of university administrations or specialist consultancies. Since strong financial incentives may attach to converting discoveries into applications here, the push is especially strong. In turn, new applications and tailored developments often suggest and spur new patterns of investigation of previously accepted knowledge.

The third feedback loop operates from discovery to integration and then via professional renewal back to discovery, with the training of new students (and especially PhDs) for positions in and outside universities functioning as a key stage at which new potentials for discovery are originated. Essentially new cohorts of students bring in new directions in discovery scholarship — and student-based external linkages with industry and society, and with other countries, often sustain them. Plato famously commented that younger people ‘are closer to ideas’ than older folk, perhaps more set in their ways. With less sunk investment in established ways of doing things, younger researchers are characteristically more willing to innovate than their elders. So it is no accident that in many disciplines student-linked and teaching-linked innovations are important stimuli for discovery scholarship — especially in some human-dominated systems where the scope for setting in train ‘social learning’ is strong, such as information technologies (Dunleavy et al, 2014). New integration work also strongly conditions how renewal of the profession operates, changing textbooks or course curricula, and differentiating the education and socialization of new cohorts of graduate students and young researchers — whose arrival at faculty level in turn often leads to methods innovations, new ideas and fresh discoveries.

The categories of scholarship formulated by Ernest Boyer (1990) are valuable as a starting point, but they are too limited in several ways. He defined scholarly integration only as pooling knowledge across disciplines, but clearly most scholarly integration occurs at the stage of pulling together ideas and concepts into coherent theories or ‘world views’ within particular disciplines. Similarly, application scholarship forms a very large part of any discipline’s activities (Stokes, 1997), going far beyond Boyer’s stress on academic service (which we see as just one form of ‘bridging’ activity). This is especially the case in disciplines focusing on ‘human-dominated systems’, a broad category that includes engineering, design, IT and computer sciences, and medical sciences, along with all the social sciences and most parts of the humanities (Dunleavy et al, 2014). Finally, the primary intellectual function served by Boyer’s ‘scholarship of teaching’ is actually a far broader ‘renewal of the profession’, inducting some of the best students to actually join the discipline and so keep its traditions and academic development alive. The sifting of new talented people, and their socialization and incorporation into the discipline, is a set of activities that includes but also extends far beyond teaching alone.

So Figure 7 shows a revised version of Boyer’s categories as four fundamental types of single-discipline scholarship, and summarizes the discussion above of the main types of academic activities involved in each.

Figure 2.7: Overview of how activities linked to four fundamental types of scholarship within single disciplines spread across conventionally recognized academic tasks

Published posts

Post 1 is a short introduction explaining the series’ overall aim of establishing some hopefully better foundations for new and established researchers to think in a more grounded or disciplined way about how academia works, the roles of disciplines and universities, and the operations of academic careers.

Post No 2 Very briefly and synoptically sketches how modern academic life and universities have evolved (over several thousand years) by embodying layers of different knowledge institutions.

Posts still to come in the next few weeks…

Post 4 looks at the importance of discipline-bridging processes — the main circuits by which inter-disciplinary work gets done, and some of the disciplinary potential for over-siloing gets modified.

Post 5 then considers the endemic competition amongst researchers and scholars, and how that relates (perhaps in unstable ways) to the co-operative and collegial elements of academia.

Post 6 considers how academic competition and collegial working both translate into the diverse career trajectories that operate within universities and disciplines.

Post 7 finishes the series by briefly exploring modern universities as bureaucratic organizations, and showing that their ‘governance’ arrangements are perhaps not as unique as academics like to suppose.

I hope that you’ll stay tuned!

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